To see the other types of publications on this topic, follow the link: Smartphone sensor integration.
Journal articles on the topic 'Smartphone sensor integration'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Smartphone sensor integration.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Raharja, Endra Putra, Mustika Irianti, Ristya Dewi Lestari, Catharina Marcelia Londong, and Sahfitri Mudumi. "Analyzing Physics Experiment using Sensor Smartphone in Traveling Carnival." Jurnal Penelitian Pendidikan IPA 10, no. 3 (2024): 1247–54. http://dx.doi.org/10.29303/jppipa.v10i3.6792.
Full textAbstract:
Smartphone integration for high school students has significant potential in physics learning. This research aims to analyze smartphone sensor data in the context of physics experiments carried out at traveling carnivals. This research uses experimental methods to compare smartphone sensor data collected via the Phyphox application with video analysis data obtained using Tracker software. The research results show the effectiveness of smartphone sensors in several carnival games, including simple leg swings, bicycle spins, kora-kora vehicles, and merry-go-rounds. Smartphone sensors can show graphs of simple harmonic motion, angular velocity, and centripetal acceleration on the game vehicle. However, limitations arise in the case of Ferris wheels and mini Ferris wheels, where smartphone sensors show reduced effectiveness caused by the instability of passenger seats during the ride. This research suggests avenues for further exploration by computing physical quantity values derived from smartphone sensor data, offering insights into potential improvements and applications in dynamic environments such as theme parks.
2
Janicka, Joanna, and Wioleta Błaszczak-Bąk. "Various scenarios of measurements using a smartphone with a LiDAR sensor in the context of integration with the TLS point cloud." Reports on Geodesy and Geoinformatics 119, no. 1 (2025): 14–22. https://doi.org/10.2478/rgg-2025-0003.
Full textAbstract:
Abstract Smartphones with Light Detection and Ranging (LiDAR) sensors are increasingly used for engineering measurements. Although the processing of the acquired point clouds seems similar to the processing of point clouds measured with, for example, a terrestrial laser scanner, processing data from a smartphone requires a special approach, first of all, when it comes to methods of obtaining and registering point clouds to obtain one complete metric point cloud. The research consisted of comparing various scenarios of measuring using a smartphone with a LiDAR sensor (a smartphone held in hand, a smartphone on a selfie stick, and a smartphone mounted on a gimbal), two acquisition strategies (one direction and zigzag) and two registration methods (point to point and cloud to cloud). The aim of the study was to find the best solution for registering the obtained point cloud with referenced terrestrial laser scanning (TLS) point cloud. It turns out that how we obtain field data using a smartphone with a LiDAR sensor is important and affects the accuracy of point cloud integration. The results showed that the use of additional devices such as a gimbal supports the data acquisition process and has an impact on the point cloud registration. In the analysed case, the RMSE registration error was the smallest and amounted to 0.012 m and 0.019 m, while the largest registration error was 0.060 m and 0.065 m, for object 1 and object 2, respectively. The result obtained using the proposed methodology can be considered satisfactory.
3
Zhao, Boxin, Xiaolong Chen, Xiaolin Zhao, Jun Jiang, and Jiahua Wei. "Real-Time UAV Autonomous Localization Based on Smartphone Sensors." Sensors 18, no. 12 (2018): 4161. http://dx.doi.org/10.3390/s18124161.
Full textAbstract:
Localization in GPS-denied environments has become a bottleneck problem for small unmanned aerial vehicles (UAVs). Smartphones equipped with multi-sensors and multi-core processors provide a choice advantage for small UAVs for their high integration and light weight. However, the built-in phone sensor has low accuracy and the phone storage and computing resources are limited, which make the traditional localization methods unable to be readily converted to smartphone-based ones. The paper aims at exploring the feasibility of the phone sensors, and presenting a real-time, less memory autonomous localization method based on the phone sensors, so that the combination of “small UAV+smartphone” can operate in GPS-denied areas regardless of the overload problem. Indoor and outdoor flight experiments are carried out, respectively, based on an off-the-shelf smartphone and a XAircraft 650 quad-rotor platform. The results show that the precision performance of the phone sensors and real-time accurate localization in indoor environment is possible.
4
Chiang, Kai-Wei, Dinh Thuan Le, Thanh Trung Duong, and Rui Sun. "The Performance Analysis of INS/GNSS/V-SLAM Integration Scheme Using Smartphone Sensors for Land Vehicle Navigation Applications in GNSS-Challenging Environments." Remote Sensing 12, no. 11 (2020): 1732. http://dx.doi.org/10.3390/rs12111732.
Full textAbstract:
Modern smartphones contain embedded global navigation satellite systems (GNSSs), inertial measurement units (IMUs), cameras, and other sensors which are capable of providing user position, velocity, and attitude. However, it is difficult to utilize the actual navigation performance capabilities of smartphones due to the low-cost and disparate sensors, software technologies adopted by manufacturers, and the significant influence of environmental conditions. In this study, we proposed a scheme that integrated sensor data from smartphone IMUs, GNSS chipsets, and cameras using an extended Kalman filter (EKF) to enhance the navigation performance. The visual data from the camera was preprocessed using oriented FAST (Features from accelerated segment test) and rotated BRIEF (Binary robust independent elementary features)-simultaneous localization and mapping (ORB-SLAM), rescaled by applying GNSS measurements, and converted to velocity data before being utilized to update the integration filter. In order to verify the performance of the integrated system, field test data was collected in a downtown area of Tainan City, Taiwan. Experimental results indicated that visual data contributed significantly to improving the accuracy of the navigation performance, demonstrating improvements of 43.0% and 51.3% in position and velocity, respectively. It was verified that the proposed integrated system, which used data from smartphone sensors, was efficient in terms of increasing navigation accuracy in GNSS-challenging environments.
5
Bautista, Nicolas, Hector Gutierrez, John Inness, and John Rakoczy. "Precision Landing of a Quadcopter Drone by Smartphone Video Guidance Sensor in a GPS-Denied Environment." Sensors 23, no. 4 (2023): 1934. http://dx.doi.org/10.3390/s23041934.
Full textAbstract:
This paper describes the deployment, integration, and demonstration of a Smartphone Video Guidance Sensor (SVGS) as a novel technology for autonomous 6-DOF proximity maneuvers and precision landing of a quadcopter drone. The proposed approach uses a vision-based photogrammetric position and attitude sensor (SVGS) to estimate the position of a landing target after video capture. A visual inertial odometry sensor (VIO) is used to provide position estimates of the UAV in a ground coordinate system during flight on a GPS-denied environment. The integration of both SVGS and VIO sensors enables the accurate updating of position setpoints during landing, providing improved performance compared with VIO-only landing, as shown in landing experiments. The proposed technique also shows significant operational advantages compared with state-of-the-art sensors for indoor landing, such as those based on augmented reality (AR) markers.
6
Silva Cotta, Joao Leonardo, Hector Gutierrez, Ivan R. Bertaska, John P. Inness, and John Rakoczy. "High-Altitude Precision Landing by Smartphone Video Guidance Sensor and Sensor Fusion." Drones 8, no. 2 (2024): 37. http://dx.doi.org/10.3390/drones8020037.
Full textAbstract:
This paper describes the deployment, integration, and demonstration of the Smartphone Video Guidance Sensor (SVGS) as novel technology for autonomous 6-DOF proximity maneuvers and high-altitude precision landing of UAVs via sensor fusion. The proposed approach uses a vision-based photogrammetric position and attitude sensor (SVGS) to support the precise automated landing of a UAV from an initial altitude above 100 m to ground, guided by an array of landing beacons. SVGS information is fused with other on-board sensors at the flight control unit to estimate the UAV’s position and attitude during landing relative to a ground coordinate system defined by the landing beacons. While the SVGS can provide mm-level absolute positioning accuracy depending on range and beacon dimensions, the proper operation of the SVGS requires a line of sight between the camera and the beacon, and readings can be disturbed by environmental lighting conditions and reflections. SVGS readings can therefore be intermittent, and their update rate is not deterministic since the SVGS runs on an Android device. The sensor fusion of the SVGS with on-board sensors enables an accurate and reliable update of the position and attitude estimates during landing, providing improved performance compared to state-of-art automated landing technology based on an infrared beacon, but its implementation must address the challenges mentioned above. The proposed technique also shows significant advantages compared with state-of-the-art sensors for High-Altitude Landing, such as those based on LIDAR.
7
Friad Qadr, Runahi, Halgurd S. Maghdid, and Azhin T. Sabir. "Novel Integration of Wi-Fi Signal and Magnetometer Sensor Measurements in Fingerprinting Technique for Indoors Smartphone positioning." ITM Web of Conferences 42 (2022): 01016. http://dx.doi.org/10.1051/itmconf/20224201016.
Full textAbstract:
Smartphones are becoming more widespread, and location-based services (LBS) have become one of the most important uses in people’s daily lives. While outdoor location is reasonably simple thanks to GNSS signals, however, indoor location is more problematic due to the lack of GNSS signals. As a result of the widespread deployment of alternative technologies such as wireless and sensors technologies, various studies on wireless-based indoor positioning have been conducted. However, each technology has its own limitations including multipath fading of wireless signals causes time-varying received signal strength as well as the accumulated error of the onboard sensors (i.e. sensor drift) resulting in poor localization accuracy. Motivated by these restrictions, this work integrates the applicability of two technologies for indoor positioning that are already available in smartphones by avoiding their limitation. The integration is based on fingerprinting-positioning technique by including magnetometer sensor measurements and WiFi signal strength. Android-based smartphones with low-cost sensors in real indoor scenarios are utilized to create a dataset and collect independent track tests to confirm results. The performance of different scenarios, such as Wi-Fi alone, magnetometer alone, and magnetometer-aided Wi-Fi, is compared. The experimental results show that the combination of magnetometer sensor and WiFi signal strength provides significant results in which leads to reducing the location error to 0.7224 meters.
8
Akbar, Firman Aulia, Mega Tri Kurnia, and Frencis Matheos Sarimole. "Rancang Bangun Purwarupa Alat Pencitraan Gerak Model Gestur Berbasis Mikrokontroler Arduino." Sistem Komputer dan Teknologi Intelegensi Artifisial (SIKOMTIA) 1, no. 3 (2023): 221–26. http://dx.doi.org/10.59039/sikomtia.v1i3.22.
Full textAbstract:
In the era of rapidly developing technology, the integration between body and technology becomes real, especially in the development of human and computer interaction. This research focuses on creating prototypes of motion imaging tools using microcontroller technology, responding to the need for more intuitive interactive tools in translating human movements. Using hardware and software engineering approaches, this study designed and tested a tool consisting of Gyrometer and Flex sensors. The gyrometer measures the orientation of motion, while the Flex sensor detects the curvature of the gesture. Tests include calibration between sensors, integration with the HC-05 Bluetooth transmission module as a wireless communication medium, and applications installed on smartphones as processing output information . The study introduced a method of gesture imaging of gestures, providing a solution to interact with computers more naturally. The urgency of this research answers the need to help translate human movement into digital input. The modeled tool is able to detect hand gestures with good accuracy. The smartphone as an output successfully displays information in the form of text and voice based on detected movements. This gesture model motion imaging tool successfully passed the testing stage, showing the effective results of the Gyrometer and Flex sensors in detecting and translating hand movements. The Bluetooth module as a data transmitter runs well sending data to an application on the smartphone, which then displays the information accurately.
9
Lazaro, Antonio, Ramon Villarino, Marc Lazaro, Nicolau Canellas, Beatriz Prieto-Simon, and David Girbau. "Recent Advances in Batteryless NFC Sensors for Chemical Sensing and Biosensing." Biosensors 13, no. 8 (2023): 775. http://dx.doi.org/10.3390/bios13080775.
Full textAbstract:
This article reviews the recent advances in the field of batteryless near-field communication (NFC) sensors for chemical sensing and biosensing. The commercial availability of low-cost commercial NFC integrated circuits (ICs) and their massive integration in smartphones, used as readers and cloud interfaces, have aroused great interest in new batteryless NFC sensors. The fact that coil antennas are not importantly affected by the body compared with other wireless sensors based on far-field communications makes this technology suitable for future wearable point-of-care testing (PoCT) devices. This review first compares energy harvesting based on NFC to other energy-harvesting technologies. Next, some practical recommendations for designing and tuning NFC-based tags are described. Power transfer is key because in most cases, the energy harvested has to be stable for several seconds and not contaminated by undesired signals. For this reason, the effect of the dimensions of the coils and the conductivity on the wireless power transfer is thoroughly discussed. In the last part of the review, the state of the art in NFC-based chemical and biosensors is presented. NFC-based tags (or sensor tags) are mainly based on commercial or custom NFC ICs, which are used to harvest the energy from the RF field generated by the smartphone to power the electronics. Low-consumption colorimeters and potentiostats can be integrated into these NFC tags, opening the door to the integration of chemical sensors and biosensors, which can be harvested and read from a smartphone. The smartphone is also used to upload the acquired information to the cloud to facilitate the internet of medical things (IoMT) paradigm. Finally, several chipless sensors recently proposed in the literature as a low-cost alternative for chemical applications are discussed.
10
Wei, Qingshan. "(Invited) Smartphone Diagnostics Meets CRISPR." ECS Meeting Abstracts MA2023-02, no. 63 (2023): 2970. http://dx.doi.org/10.1149/ma2023-02632970mtgabs.
Full textAbstract:
Smartphone-based optical imaging and sensing devices are among the next-generation biosensors that have shown great potential to transform the field of point-of-care (POC) diagnostics. With the rapid improvement of hardware (e.g., lens, image sensor, and CPU), smartphone has become a transformative microscopy and sensing platform that can support various detection or biomedical measurement applications, especially for resource-limited settings. On the other side, smartphone diagnostics can maximize its potential for early disease detection by coupling with specific molecular assays, such as nucleic acid amplification tests and immunoassays. This talk will highlight our recent effort in creating advanced field-portable biosensor platforms based on smartphones and the integration of smartphone device with CRISPR assay to generate a new functional POC diagnostic platform. An unexpected trans-cleavage behavior of Cas12a against double-stranded DNA substrates is discovered, which greatly eases the design of CRISPR-Dx reporter molecules. Promising applications ranging from POC diagnostics of human diseases to rapid assessment of biomanufacturing products will also be illustrated. Figure 1
11
Djizi, Hamza, Zoubir Zahzouh, and Azzedine Bouzaouit. "Labview and Remotexy Integration for Quadrotor Stabilization and Control." Scientific Bulletin of Electrical Engineering Faculty 23, no. 1 (2023): 9–14. http://dx.doi.org/10.2478/sbeef-2023-0002.
Full textAbstract:
Abstract Nowadays, Small quadcopters have made significant advancements in recent years, thanks to the development of control systems, the availability of sensors, and affordable and reliable materials for their production. Additionally, programs have been developed to model and analyze these aircraft before production. The professional applications of quadcopters are seemingly endless due to their many advantages. The aim of this research is to build a quadcopter and test its stability utilizing Arduino Mega, IMU sensor (Inertial Measurement Unit) and MPU-6050 in LabVIEW environment. The objective is to select the suitable PID parameters and create a remote-control program that can be operated using a smartphone and RemoteXY app on Android OS.
12
Badriah Nursakinah. "Implementation of IoT (Internet of Thing) In The Design of a Smart Robot Prototype For Automatic Waste Disposal Based on Arduino At Mega." International Journal of Integrative Sciences 3, no. 2 (2024): 209–22. http://dx.doi.org/10.55927/ijis.v3i2.8203.
Full textAbstract:
The background of this research is to design a prototype for an automatic waste disposal robot based on Arduino AT Mega which is integrated with IoT (Internet of Things). Where the Internet of Things can generally be interpreted as objects around us that can communicate with each other via the internet network. Internet Of Thing has the concept of expanding the benefits of being connected to a continuous internet connection. The aim of this research is to design a prototype for an automatic waste disposer that was developed by integrating with IoT technology. The input process is carried out as an integration process between the Arduino and the HC-SR04 sensor and the Infrared Line Tracking sensor. So that the Arduino and HCSR 04 and Line Tracking sensors can work optimally, the main thing to do is carry out the configuration process using a beardboard and jumper cables as well as LED lights to ensure whether the sensors and other supporting tools are integrated with the Arduino. Where in the integration process between the Arduino buit and the ESP8266 so that the movement of the prototype can be easily monitored only via a smartphone or laptop even remotely
13
Alqaydi, Saif, Waleed Zeiada, Ahmed El Wakil, Ali Juma Alnaqbi, and Abdelhalim Azam. "A Comprehensive Review of Smartphone and Other Device-Based Techniques for Road Surface Monitoring." Eng 5, no. 4 (2024): 3397–426. https://doi.org/10.3390/eng5040177.
Full textAbstract:
Deteriorating road infrastructure is a global concern, especially in low-income countries where financial and technological constraints hinder effective monitoring and maintenance. Traditional methods, like inertial profilers, are expensive and complex, making them unsuitable for large-scale use. This paper explores the integration of cost-effective, scalable smartphone technologies for road surface monitoring. Smartphone sensors, such as accelerometers and gyroscopes, combined with data preprocessing techniques like filtering and reorientation, improve the quality of collected data. Machine learning algorithms, particularly CNNs, are utilized to classify road anomalies, enhancing detection accuracy and system efficiency. The results demonstrate that smartphone-based systems, paired with advanced data processing and machine learning, significantly reduce the cost and complexity of traditional road surveys. Future work could focus on improving sensor calibration, data synchronization, and machine learning models to handle diverse real-world conditions. These advancements will increase the accuracy and scalability of smartphone-based monitoring systems, particularly for urban areas requiring real-time data for rapid maintenance.
14
Shuqian, Dou, Liu Long, Liu Fangyi, et al. "16‐2: Invited Paper: Ambient Light Sensor Integration in a‐Si LCD with Regular Processing." SID Symposium Digest of Technical Papers 54, no. 1 (2023): 199–201. http://dx.doi.org/10.1002/sdtp.16524.
Full textAbstract:
A solution for ambient light sensor (ALS) integration in a‐Si LCD with cost advantage and nearly the same performance as external ALS solution. In this work we provide practical solution for the most challenged smartphone
15
Winanti, Andhika Fernando, Dhimas Yudhatama, et al. "Early Flood Detection with SIMOBI: IoT and Mobile Integration." Jurnal Pekommas 9, no. 2 (2024): 201–7. https://doi.org/10.56873/jpkm.v9i2.5433.
Full textAbstract:
Tangerang Regency as a city of a thousand industries as well as a buffer for the capital city, is densely populated and often experiences flooding every rainy season. Losses from flood disasters can cause damage and disruption of economic activities and even loss of life. Efforts to minimize losses and loss of life due to flood disasters with early detection tools through the Flood Monitoring Application (SIMOBI). The purpose of creating SIMOBI for early detection of floods so that damage, disruption of economic activities and loss of life can be minimized. This research is a system development to find solutions for early flood detection in Tangerang Regency using the Prototype method and the system results are validated through Black Box Testing. This system is designed using the IoT (Internet of Things) principle which uses an internet network to connect the NodeMCU ESP 8266 microprocessor device with sensor and actuator devices. The NodeMCU ESP 8266 microprocessor to read water level data provided by the Ultrasonic sensor then sends data using the internet to a Smartphone with software that has been created to provide flood indication notifications. In addition to notifications from the Smartphone, a buzzer or siren is used to make a sound if there is an indication of flooding. The system works automatically if the water level exceeds the normal limit at the location and will send a notification via Smartphone and voice. This system is implemented in various flood-prone areas in Tangerang Regency such as Balaraja, Pasar Kemis, Cikupa, Curug, Cisoka, Gunung Kaler, Kelapa Dua, Jayanti, and Jambe.
16
Ustun, Ilyas, and Mecit Cetin. "Speed Estimation using Smartphone Accelerometer Data." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 3 (2019): 65–73. http://dx.doi.org/10.1177/0361198119836977.
Full textAbstract:
This paper is focused on developing an algorithm to estimate vehicle speed from accelerometer data generated by an onboard smartphone. The kinetic theory tells that the integration of acceleration gives the speed of a vehicle. Thus, the integration of the acceleration values collected with the smartphone in the direction of motion would theoretically yield the speed. However, speed estimation by the integration of accelerometer data will not yield accurate results, as the accelerometer data in the direction of motion is not pure acceleration, but involves white noise, phone sensor bias, vibration, gravity component, and other effects. To account for these sources of noise and error, a calibration method that can adjust the speed at certain times or points is needed. The exact times when the vehicle stops and starts are identified and used to calibrate the estimated speed. Based on the collected sample data, the proposed method yields that the estimated speed is on average within 10 mph of the actual speed, with a lower margin at the street-level driving. This suggests that with more information to calibrate the speed, the model accuracy can be improved further.
17
Zulkipli, Muhammad Asyraf, Khairul Huda Yusof, Almadora Anwar Sani, and Muhammad Auzi Herizal. "Development of IoT Based Clothesline using Microcontroller." International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS) 1, no. 1 (2023): 12–18. http://dx.doi.org/10.53893/ijmeas.v1i1.217.
Full textAbstract:
Sunlight has long been used in daily activities, especially drying clothes. The only concern is sudden rain and people are not aware that their clothes are still on the clothesline. Due to this, many opt to dry their clothes indoor, which takes longer to dry. With regard to the aforementioned issue, in this research, Internet of Things (IoT) concept is applied in the production of automatic clotheslines by the integration of weather monitoring and forecasting with automatic clothesline. This automatic clothesline has an LDR sensor and a rain sensor to detect light and raindrops in the environment, then the values obtained from these two sensors are processed by the microcontroller to control the DC motor that can moves the clothes inside and outside from the sensors system which can be monitored and controled via smartphone using the Blynk app. The outcome of this research is an automatic clothesline prototype that is accessible through the Blynk app; utilising LDR sensors, rain sensors, and motor to retrieve clothes.
18
Putri, Renny Eka, Widi Darmadi, Dinah Cherie, and Aninda Tifani Puari. "The Design of Automatic Soil pH Control System on Aloe vera Cultivation with an Integration of Internet of Things (IoT)." Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) 12, no. 3 (2023): 597. http://dx.doi.org/10.23960/jtep-l.v12i3.597-609.
Full textAbstract:
Machine learning and internet of thing (IoT) would be the best option for monitoring the appropriate soil pH condition. This research aimed on the design an automatic soil pH control system based on IoT for monitoring the cultivation of Aloe vera plants. The Telegram application was occupied as an IoT platform and was connected to a free and easy access application, Node MCU 8266. Furthermore, relay, Arduino Uno and smartphone were occupied in the system. According to the system testing, soil pH sensor readings are close to the actual value as evidenced by the linear regression value or R2 on sensors 1 and 2 which are close to 1. Meanwhile, the total percentage of system performance testing was 93% while the error value for the pH sensors were 0.96 and 1.6% for sensor 1 and sensor 2, respectively. Furthermore, the plant observations showed that the average leaf length of plants with a control system was 24.78 cm while with the manual system was 23.11 cm. From the results of the T test obtained, it was found that the control system applied to Aloe vera cultivation had a significant effect on the growth and development of Aloe vera compared to Aloe vera plants with a manual system. Keywords: Aloe vera, Control system, Internet of things (IoT), Soil pH sensor
19
Gregorius Dwi Perkasa and Felix Yustian Setiono. "<i>ADVANCED LINE FOLLOWER ROBOT</i> DENGAN SENSOR ULTRASONIK UNTUK <i>DYNAMIC OBSTACLE AVOIDANCE</i> DAN <i>SMARTPHONE-BASE</i>D CONTROL." Elektrika 17, no. 1 (2025): 12–20. https://doi.org/10.26623/elektrika.v17i1.11813.
Full textAbstract:
This research aims to develop functions for a line follower robot by adding the ability to detect objects in front of the robot using an HC-SR04 ultrasonic sensor, enabling the robot to stop and avoid collisions. In addition, the robot is equipped with a smartphone control function using Bluetooth. With the ultrasonic sensor positioned at the front of the robot, it can detect the distance of objects ahead, allowing the robot to stop its wheel movement when an object is detected nearby. In addition, the integration of the HC-05 Bluetooth module into the robot's system enables wireless control of the robot's movement via Bluetooth commands sent from a smartphone application. The methods used in this research include designing the robot's hardware circuitry, programming the Arduino microcontroller, developing a smartphone application, and testing the robot's performance in various scenarios. The results of the tests show that the robot can accurately follow the designated path, stop when an obstacle is detected in front of it, and respond to commands from the smartphone. The additional features implemented in this research extend the robot's functionality beyond simply following a predefined path. Keywords: Bluetooth HC-05, Robot Line Follower, Smartphone Control, Ultrasonic Sensor ABSTRAK Penelitian ini memiliki tujuan untuk mengembangkan fitur pada robot line follower dengan penambahan fitur mendeteksi benda atau objek didepan robot menggunakan sensor ultrasonic HC-SR04 agar robot dapat berhenti dan tidak membentur objek tersebut, serta penambahan fitur control robot menggunakan smartphone menggunakan bluetooth. Dengan adanya sensor ultrasonik yang terletak pada bagian depan robot, maka robot dapat mendeteksi jarak objek yang berada didepan robot sehingga robot mampu memiliki kemampuan untuk menghentikan gerakan roda Ketika terdeteksi ada objek dekat yang berada didepan robot. Selain itu dengan adanya modul Bluetooth HC-05 yang terintegrasi dengan sistem pada robot, maka robot dapat dikendalikan arah geraknya secara nirkabel menggunakan protocol Bluetooth yang diperintahkan melalui aplikasi pada smartphone. Metode yang digunakan pada penelitian ini meliputi perancangan rangkaian perangkat keras pada robot, pemrograman mikrokontroler Arduino, pembuatan aplikasi pada smartphone serta pengujian kinerja robot pada beberapa sekenario yang diujikan. Hasil dari pengujian yang dilakukan pada penelitian ini, menunjukan bahwa robot dapat mengikuti garis lintasan yang yang dibuat serta mampu berhenti Ketika terdapat objek yang menghalangi pergerakan robot dari sisi depan, dan robot mampu untuk perespon perintah yang dikendalikan dari smartphone. Penambahan fitur-fitur yang diberikan pada penelitian ini, menjadikan robot memiliki fungsi yang lebih dari sekedar bergerak mengikuti lintasan yang telah ada.
20
Kiran, Anju Ranolia, Priyanka, et al. "An indolium inspired portable colorimetric sensor for cyanide recognition in environmental samples with smartphone integration." RSC Advances 15, no. 12 (2025): 9129–40. https://doi.org/10.1039/d5ra00576k.
Full text
21
K. V, Shivakumar. "Smart Floor Cleaning System: A Review." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 764–68. https://doi.org/10.22214/ijraset.2024.65894.
Full textAbstract:
The automatic floor cleaner described above integrates a number of components to efficiently travel and clean interior spaces. The main purpose of this device is to utilizing an Arduino microcontroller, synchronize the activities of many sensors and actuators. The ultrasonic sensor serves as the primary instrument for detecting obstacles and determining the cleaner's separation from walls or other objects. At the same time, the infrared sensor enhances detection capabilities, particularly when there are surface flaws or low-lying impediments. Communication and control are made easier by the integration of a Bluetooth module, which offers wireless connectivity with a smartphone or other compatible devices. Users can remotely operate the cleaner, adjust its settings, or monitor its condition using a certain smartphone application. The device's rechargeable battery power enables portability and independence while cleaning. The cleaning mechanism is powered by a BLDC (Brushless DC) motor, which is renowned for its dependability and efficiency. When used in conjunction with an electronic speed controller and motor driver, the motor's direction and speed can be precisely controlled. This setup allows the cleaner to adapt its cleaning strategy to shifting environmental conditions or user preferences.
22
Blum, Sarah, Daniel Hölle, Martin Georg Bleichner, and Stefan Debener. "Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer." Sensors 21, no. 23 (2021): 8135. http://dx.doi.org/10.3390/s21238135.
Full textAbstract:
The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices.
23
Bravo-Yagüe, Juan Carlos, Gema Paniagua-González, Rosa María Garcinuño, Asunción García-Mayor, and Pilar Fernández-Hernando. "Colorimetric Molecularly Imprinted Polymer-Based Sensors for Rapid Detection of Organic Compounds: A Review." Chemosensors 13, no. 5 (2025): 163. https://doi.org/10.3390/chemosensors13050163.
Full textAbstract:
This review offers a comprehensive examination of the development and current state of the art in the field of molecularly imprinted polymer (MIP)-based colorimetric sensors, focusing on their potential for the rapid detection of organic compounds. These MIP-sensors are gaining considerable attention due to their distinctive capacity to modify sensor surfaces by creating recognition cavities within the polymer matrix, providing a versatile and highly selective platform for detecting a broad spectrum of analytes. This review systematically examines different types of MIP-based colorimetric sensors, attending to the target analyte, highlighting their applications in on-site sample detection, drug monitoring, environmental analysis, and food safety detection. The integration of novel technologies, such as nanozymes and smartphone-based detection, which enhance the capabilities of colorimetric MIP sensors, is also addressed. The sensors are particularly valuable due to their low cost, rapid response times, portability, and ease of use. Finally, the review outlines the future challenges for the development of MIP-based colorimetric sensors, focusing on overcoming existing limitations, improving sensor performance, and expanding their applications across various fields.
24
Aji, Adi Sasmito, Triwilaswandio Wuruk Pribadi, and Imam Baihaqi. "Integration of Hand Motion Sensor, Artificial Intelligence, and QR Code for Real-Time Monitoring of Welder’s Performance and Welding Quality: A Conceptual Framework." IOP Conference Series: Earth and Environmental Science 1423, no. 1 (2024): 012035. https://doi.org/10.1088/1755-1315/1423/1/012035.
Full textAbstract:
Abstract The welding quality, as the product of manual or semi-automatic welding process, is highly affected by the hand motion of the welders. In the shipbuilding industry, the welding inspection process is commonly performed after the welding process is finished by the welding inspectors. The research aims to develop a realtime monitoring system for welder’s performance by recording their hand motion. The hand motion sensors and QR codes are integrated through the Internet of Things (IoT) to monitor the welder’s performance and weldment quality. The realtime monitoring concept was developed by designing the monitoring concept utilising the QR code and Android mobile smartphone. The welder’s hands are equipped with the Inertial Measurement Unit (IMU) sensor to record their hand motion. The motion data are transferred directly from the IMU sensor to the smartphone storage through a Bluetooth connection. The data then are uploaded to the cloud storage through the internet connection. The stored recorded data was then analysed and compared with the qualified welders’ hand motions. After welding ends, the welder reports the visual appearance of the weldment and its location as identified by the QR code through the Android system. Finally, the functionality of the application prototype is tested, and the results show that the system can be used practically for real-time monitoring of welder performance and welding quality.
25
Singhal, Chirag M., Vani Kaushik, Abhijeet Awasthi, et al. "Deep Learning-Enhanced Portable Chemiluminescence Biosensor: 3D-Printed, Smartphone-Integrated Platform for Glucose Detection." Bioengineering 12, no. 2 (2025): 119. https://doi.org/10.3390/bioengineering12020119.
Full textAbstract:
A novel, portable chemiluminescence (CL) sensing platform powered by deep learning and smartphone integration has been developed for cost-effective and selective glucose detection. This platform features low-cost, wax-printed micro-pads (WPµ-pads) on paper-based substrates used to construct a miniaturized CL sensor. A 3D-printed black box serves as a compact WPµ-pad sensing chamber, replacing traditional bulky equipment, such as charge coupled device (CCD) cameras and optical sensors. Smartphone integration enables a seamless and user-friendly diagnostic experience, making this platform highly suitable for point-of-care (PoC) applications. Deep learning models significantly enhance the platform’s performance, offering superior accuracy and efficiency in CL image analysis. A dataset of 600 experimental CL images was utilized, out of which 80% were used for model training, with 20% of the images reserved for testing. Comparative analysis was conducted using multiple deep learning models, including Random Forest, the Support Vector Machine (SVM), InceptionV3, VGG16, and ResNet-50, to identify the optimal architecture for accurate glucose detection. The CL sensor demonstrates a linear detection range of 10–1000 µM, with a low detection limit of 8.68 µM. Extensive evaluations confirmed its stability, repeatability, and reliability under real-world conditions. This deep learning-powered platform not only improves the accuracy of analyte detection, but also democratizes access to advanced diagnostics through cost-effective and portable technology. This work paves the way for next-generation biosensing, offering transformative potential in healthcare and other domains requiring rapid and reliable analyte detection.
26
Adhau, Dr Sarla. "Empowering Homes with Esp-32." International Scientific Journal of Engineering and Management 03, no. 04 (2024): 1–9. http://dx.doi.org/10.55041/isjem01662.
Full textAbstract:
Smartboard system utilizing an ESP32 microcontroller integrated with various sensors and electric sockets. The system aims to enhance safety, convenience, and energy efficiency in residential environments. Initially, an electric switch box was fitted with two sockets, and an ESP32 microcontroller was integrated to control them. The system further includes a smoke sensor for gas leakage detection, water level magnetic sensors for automatic water pump control, a DHT sensor for temperature and humidity monitoring, a display for real-time data visualization, and a Miniature Circuit Breaker (MCB) for protection. Through integration with the Blynk app, users can remotely monitor and control the system, including supplying power to sockets and detecting gas leaks. Additionally, Bluetooth connectivity is incorporated for added convenience, particularly for electric scooter users in complex living environments. Experimental results demonstrate the efficacy of the integrated system in providing remote control, safety monitoring, and automation features. KeyWords: Smartphone-based, Mobile application, an automated system (ESP32), Wifi Network
27
Ali, Shaukat, Shah Khusro, Irfan Ullah, Akif Khan, and Inayat Khan. "SmartOntoSensor: Ontology for Semantic Interpretation of Smartphone Sensors Data for Context-Aware Applications." Journal of Sensors 2017 (2017): 1–26. http://dx.doi.org/10.1155/2017/8790198.
Full textAbstract:
The integration of cheap and powerful sensors in smartphones has enabled the emergence of several context-aware applications and frameworks. However, the available smartphone context-aware frameworks are static because of using relational data models having predefined usage of sensory data. Importantly, the frameworks lack the soft integration of new data types and relationships that appear with the emergence of new smartphone sensors. Furthermore, sensors generate huge data that intensifies the problem of too much data and not enough knowledge. Smarting of smartphone sensory data is essential for advanced analytical processing, integration, inferencing, and interpretation by context-aware applications. In order to achieve this goal, novel smartphone sensors ontology is required for semantic modeling of smartphones and sensory data, which is the main contribution of this paper. This paper presents SmartOntoSensor, a lightweight mid-level ontology that has been developed using NeOn methodology and Content Ontology Design pattern. The ontology describes smartphone and sensors from different aspects including platforms, deployments, measurement capabilities and properties, observations, data fusion, and context modeling. SmartOntoSensor has been developed using Protégé and evaluated using OntoQA, SPARQL, and experimental study. The ontology is also tested by integrating into ModeChanger application that leverages SmartOntoSensor for automatic changing of smartphone modes according to the varying contexts. We have obtained promising results that advocate for the improved ontological design and applications of SmartOntoSensor.
28
Eko, Didik Widianto, Nurul Huda Gayuh, and Dwi Nurhayati Oky. "Portable spirometer using pressure-volume method with Bluetooth integration to Android smartphone." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 3977–86. https://doi.org/10.11591/ijece.v13i4.pp3977-3986.
Full textAbstract:
This paper presents a study on an embedded spirometer using the low-cost MPX5100DP pressure sensor and an Arduino Uno board to measure the air exhaled flow rate and calculate force vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and the FEV1/FVC ratio of human lungs volume. The exhaled air flow rate was measured from differential pressure in the sections of a mouthpiece tube using the venturi effect equation. This constructed mouthpiece and the embedded spirometer resulted in a 96.27% FVC reading accuracy with a deviation of 0.09 L and 98.05% FEV1 accuracy with a deviation of 0.05 L compared to spirometry. This spirometer integrates an HC-05 Bluetooth module for spirometry data transceiving to a smartphone for display and recording in an Android application for further chronic obstructive pulmonary disease (COPD) diagnosis.
29
Ho, H. Y., H. F. Ng, Y. T. Leung, W. Wen, L. T. Hsu, and Y. Luo. "SMARTPHONE LEVEL INDOOR/OUTDOOR UBIQUITOUS PEDESTRIAN POSITIONING 3DMA GNSS/VINS INTEGRATION USING FGO." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W1-2023 (May 25, 2023): 175–82. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-175-2023.
Full textAbstract:
Abstract. This paper discusses ubiquitous smartphone pedestrian positioning challenges in urban canyons and GNSS-denied areas such as indoor spaces. Existing sensor-based techniques, including GNSS, INS, and VIO, have limitations that affect positioning accuracy and reliability. A machine learning-based approach is suggested to employ Support Vector Machine (SVM) to classify indoor/outdoor (IO) detection using GNSS measurement data. The proposed system integrates local estimates on VIO and 3D mapping aided (3DMA) GNSS measurements using Factor Graph Optimization (FGO) with an IO detection switch to estimate precise pose and eliminate global drift. The effectiveness of the system is evaluated through real-world experiments that produce notable outcomes.
30
Widianto, Eko Didik, Gayuh Nurul Huda, and Oky Dwi Nurhayati. "Portable spirometer using pressure-volume method with Bluetooth integration to Android smartphone." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 3977. http://dx.doi.org/10.11591/ijece.v13i4.pp3977-3986.
Full textAbstract:
<span lang="EN-US">This paper presents a study on an embedded spirometer using the low-cost MPX5100DP pressure sensor and an Arduino Uno board to measure the air exhaled flow rate and calculate force vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and the FEV1/FVC ratio of human lungs volume. The exhaled air flow rate was measured from differential pressure in the sections of a mouthpiece tube using the venturi effect equation. This constructed mouthpiece and the embedded spirometer resulted in a 96.27% FVC reading accuracy with a deviation of 0.09 L and 98.05% FEV1 accuracy with a deviation of 0.05 L compared to spirometry. This spirometer integrates an HC-05 Bluetooth module for spirometry data transceiving to a smartphone for display and recording in an Android application for further chronic obstructive pulmonary disease (COPD) diagnosis.</span>
31
Li, Jinke, Botao Xie, and Xuefeng Zhao. "A Method of Interstory Drift Monitoring Using a Smartphone and a Laser Device." Sensors 20, no. 6 (2020): 1777. http://dx.doi.org/10.3390/s20061777.
Full textAbstract:
Interstory drift is an important engineering parameter in building design and building structural health monitoring. However, many problems exist in current interstory drift monitoring methods. The traditional method is imprecise—double numerical integration of acceleration data—and other direct monitoring methods need professional equipment. This paper proposes a method to solve these problems by monitoring the interstory drift with a smartphone and a laser device. In this method, a laser device is installed on the ceiling while a smartphone is fixed on a steel projection plate on the floor. Compared with a reference sensor, the method designed in this study shows that a smartphone is competent in monitoring the interstory drift. This method utilizes a smartphone application (APP) named D-Viewer to implement monitoring and data storage just in one place, which is also inexpensive. The results showed that this method has an average percent error of 3.37%, with a standard deviation of 2.67%. With the popularization of the smartphone, this method is promising in acquiring large amounts of data, which will be significant for building assessment after an earthquake.
32
Doreen G., Nyakairu. "Smartphone-Based Diagnostic Tools: Revolutionizing Healthcare Access." RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES 4, no. 3 (2024): 52–56. https://doi.org/10.59298/rijses/2024/435256.
Full textAbstract:
Smartphone-based diagnostic tools have emerged as transformative innovations in healthcare, offering accessible, cost-effective solutions for disease diagnosis, remote monitoring, and patient engagement. This narrative review examines the capabilities, benefits, and limitations of these tools in healthcare delivery. We discuss their role in addressing disparities in healthcare access, particularly in resource-limited and remote areas, and their potential to support early detection and management of chronic diseases. Challenges such as data privacy, technological accessibility, and regulatory gaps are also analyzed. The review emphasizes the importance of multidisciplinary collaborations to optimize and scale these tools, addressing ethical concerns and ensuring equitable healthcare delivery. Future directions include advancements in sensor technology, artificial intelligence integration, and policy adaptations to harness the full potential of smartphone-based diagnostics. Keywords: Smartphone diagnostics, Mobile health (mHealth), Telemedicine, Remote monitoring, Early disease detection.
33
Alo, Uzoma Rita, Henry Friday Nweke, Ying Wah Teh, and Ghulam Murtaza. "Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System." Sensors 20, no. 21 (2020): 6300. http://dx.doi.org/10.3390/s20216300.
Full textAbstract:
Human motion analysis using a smartphone-embedded accelerometer sensor provided important context for the identification of static, dynamic, and complex sequence of activities. Research in smartphone-based motion analysis are implemented for tasks, such as health status monitoring, fall detection and prevention, energy expenditure estimation, and emotion detection. However, current methods, in this regard, assume that the device is tightly attached to a pre-determined position and orientation, which might cause performance degradation in accelerometer data due to changing orientation. Therefore, it is challenging to accurately and automatically identify activity details as a result of the complexity and orientation inconsistencies of the smartphone. Furthermore, the current activity identification methods utilize conventional machine learning algorithms that are application dependent. Moreover, it is difficult to model the hierarchical and temporal dynamic nature of the current, complex, activity identification process. This paper aims to propose a deep stacked autoencoder algorithm, and orientation invariant features, for complex human activity identification. The proposed approach is made up of various stages. First, we computed the magnitude norm vector and rotation feature (pitch and roll angles) to augment the three-axis dimensions (3-D) of the accelerometer sensor. Second, we propose a deep stacked autoencoder based deep learning algorithm to automatically extract compact feature representation from the motion sensor data. The results show that the proposed integration of the deep learning algorithm, and orientation invariant features, can accurately recognize complex activity details using only smartphone accelerometer data. The proposed deep stacked autoencoder method achieved 97.13% identification accuracy compared to the conventional machine learning methods and the deep belief network algorithm. The results suggest the impact of the proposed method to improve a smartphone-based complex human activity identification framework.
34
Lovell, N. H., G. Z. Yang, A. Horsch, et al. "What Does Big Data Mean for Wearable Sensor Systems?" Yearbook of Medical Informatics 23, no. 01 (2014): 135–42. http://dx.doi.org/10.15265/iy-2014-0019.
Full textAbstract:
Summary Objectives:The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. Methods: The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Health-care Working Group. Results: The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. Conclusions: Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.
35
EGORCHEV, A. A., D. E. CHIKRIN, D. M. PASHIN, and A. F. FAKHRUTDINOV. "ALGORITHM FOR DETECTION OF HEAD TREMOR ACCORDING TO DATA OF A SMARTPHONE VIDEO CAMERA OF A BIOMEDICAL MONITORING SYSTEM." Computational nanotechnology 11, no. 4 (2024): 87–93. https://doi.org/10.33693/2313-223x-2024-11-4-87-93.
Full textAbstract:
Modern conditions demand active digitization from humanity across various spheres of activity and daily life, facilitating faster task completion and simplifying processes. Self-diagnosis allows individuals to identify symptoms, which can serve as a basis for consulting medical professionals, especially crucial in critical situations where lives are at stake. Thus, it is clear that the development of such systems is a relevant challenge. In this context, head tremor plays a significant role as it may indicate the presence of Parkinson’s disease or multiple sclerosis. The aim of this work is to develop a head tremor detection module suitable for integration into smartphone applications. The study employs a method based on analyzing data from the optical sensor, namely the front camera of the smartphone. This method utilizes an open machine learning model, ML Kit, for facial recognition, along with a specially designed algorithm for processing results. Testing demonstrated an accuracy of 0.92 according to the accuracy metric. This approach offers a novel method for detecting head tremors and highlights the effectiveness of using ML Kit’s standard model for similar tasks on smartphones, which can also be applied within a larger biomedical diagnostic system.
36
Ku, Minjae, Joohee Kim, Jong-Eun Won, et al. "Smart, soft contact lens for wireless immunosensing of cortisol." Science Advances 6, no. 28 (2020): eabb2891. http://dx.doi.org/10.1126/sciadv.abb2891.
Full textAbstract:
Despite various approaches to immunoassay and chromatography for monitoring cortisol concentrations, conventional methods require bulky external equipment, which limits their use as mobile health care systems. Here, we describe a human pilot trial of a soft, smart contact lens for real-time detection of the cortisol concentration in tears using a smartphone. A cortisol sensor formed using a graphene field-effect transistor can measure cortisol concentration with a detection limit of 10 pg/ml, which is low enough to detect the cortisol concentration in human tears. In addition, this soft contact lens only requires the integration of this cortisol sensor with transparent antennas and wireless communication circuits to make a smartphone the only device needed to operate the lens remotely without obstructing the wearer’s view. Furthermore, in vivo tests using live rabbits and the human pilot experiment confirmed the good biocompatibility and reliability of this lens as a noninvasive, mobile health care solution.
37
Talebi-Kalaleh, Mohammad, and Qipei Mei. "Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data." Structural Control and Health Monitoring 2024 (April 4, 2024): 1–23. http://dx.doi.org/10.1155/2024/5436675.
Full textAbstract:
Traditional bridge health monitoring methods that necessitate sensor installation are not only costly but also time-consuming. In contrast, utilizing smartphone data collected from vehicles as they traverse bridges offers an efficient and cost-effective alternative. This paper introduces a cutting-edge damage detection framework for indirect monitoring of bridge structures, leveraging a substantial volume of acceleration data collected from smartphones in vehicles passing over the bridge. Our innovative approach addresses the challenge of collecting and transmitting high-frequency data while preserving smartphone battery life and data plans through the integration of compressed sensing (CS) into the crowdsensing-based monitoring framework. CS employs random sampling and signal recovery from a significantly reduced number of samples compared to the requirements of the Nyquist–Shannon sampling theorem. In the proposed framework, acceleration signals from vehicles are initially acquired using smartphone sensors, undergo compression, and are then transmitted for signal reconstruction. Subsequently, feature extraction and dimensionality reduction are performed using Mel-frequency cepstral coefficients and principal component analysis. Damage indexes are computed based on the dissimilarity between probability distribution functions utilizing the Wasserstein distance metric. The efficacy of the proposed methodology in bridge monitoring has been substantiated through the utilization of numerical models and a lab-scale bridge. Furthermore, the feasibility of implementing the framework in a real-world application has been investigated, leveraging the smartphone data from 102 vehicle trips on the Golden Gate Bridge. The results demonstrate that damage detection using the reconstructed signals obtained through compressed sensing achieves comparable performance to that obtained with the original data sampled at the Nyquist measurement sampling rate. However, it is observed that to retain severity information within the signals for accurate damage severity identification, the compression level should be limited to 20%. These findings affirm that compressed sensing significantly reduces the data collection requirements for crowdsensing-based monitoring applications, without compromising the accuracy of damage detection while preserving essential damage-sensitive information within the dataset.
38
Raharja, Endra Putra, Edi Sutomo, Febrian Andi Hidayat, Asih Kasan, and Nia Mangkasa. "Smartphone Sensor-Based Physics Module for Hands-On Learning in Waves and Optics." Jurnal Penelitian Pendidikan IPA 11, no. 3 (2025): 580–90. https://doi.org/10.29303/jppipa.v11i3.10240.
Full textAbstract:
The limited laboratory equipment in many schools hinders students in conducting physics experiments, so that learning is more theoretical and less supportive of practice-based understanding. This study aims to develop a smartphone sensor-based physics experiment module on wave and optical materials that is valid, practical, and effective, and allows students to conduct experiments flexibly and independently. This research uses the Research and Development (R & D) method with the Borg & Gall model. Validation by media and material experts showed that this module has met the eligibility standards, with a score of 3.67 (73.4%) for media aspects and 3.56 (71.2%) for material aspects. The practicality test showed that this module can be used well in experimental learning, with a score of 3.48 (69.6%) in the “Good” category. Evaluation of effectiveness through comparison of pre-test and post-test showed an increase in learning outcomes with an N-gain of 0.55 (medium category). These results indicate that the integration of smartphone sensors in physics experiments can be an alternative solution for schools with limited laboratory facilities. This module offers a more flexible, accessible and technology-based learning approach. Future research can develop similar modules for other physics materials and evaluate their effectiveness in various learning scenarios.
39
Juneau, Pascale, Natalie Baddour, Helena Burger, and Edward D. Lemaire. "Balance confidence classification in people with a lower limb amputation using six minute walk test smartphone sensor signals." PLOS Digital Health 3, no. 8 (2024): e0000570. http://dx.doi.org/10.1371/journal.pdig.0000570.
Full textAbstract:
The activities-specific balance confidence scale (ABC) assesses balance confidence during common activities. While low balance confidence can result in activity avoidance, excess confidence can increase fall risk. People with lower limb amputations can present with inconsistent gait, adversely affecting their balance confidence. Previous research demonstrated that clinical outcomes in this population (e.g., stride parameters, fall risk) can be determined from smartphone signals collected during walk tests, but this has not been evaluated for balance confidence. Fifty-eight (58) individuals with lower limb amputation completed a six-minute walk test (6MWT) while a smartphone at the posterior pelvis was used for signal collection. Participant ABC scores were categorized as low confidence or high confidence. A random forest classified ABC groups using features from each step, calculated from smartphone signals. The random forest correctly classified the confidence level of 47 of 58 participants (accuracy 81.0%, sensitivity 63.2%, specificity 89.7%). This research demonstrated that smartphone signal data can classify people with lower limb amputations into balance confidence groups after completing a 6MWT. Integration of this model into the TOHRC Walk Test app would provide balance confidence classification, in addition to previously demonstrated clinical outcomes, after completing a single assessment and could inform individualized rehabilitation programs to improve confidence and prevent activity avoidance.
40
Sintiyah, Elisa Ayu, Ajesh Faizal, Sumber Sumber, and Singgih Yudha Setiawan. "Wireless Blood Pressure Monitor with Android Integration: Tracking Systolic and Diastolic Parameters." Jurnal Teknokes 17, no. 2 (2024): 107. http://dx.doi.org/10.35882/teknokes.v17i2.553.
Full textAbstract:
Blood pressure measurement plays a crucial role in detecting underlying diseases in the human body. It enables the identification of conditions like heart failure, kidney failure, liver damage, and stroke, underscoring the importance of regular measurements. To facilitate independent and routine blood pressure monitoring, the development of an automatic blood pressure measuring device is essential. This research aims to design and fabricate a digital sphygmomanometer that can transmit measurements to a smartphone through the Blynk application. The blood pressure measurement is conducted using the MPX5050GP pressure sensor as the pressure detector. The device is programmed using the Esp32 microcontroller and incorporates an LCD screen to display the measurement results. The study involved measuring six participants, with each individual's blood pressure recorded ten times. The obtained measurements were then compared to those of the Omron HEM-7120 digital sphygmomanometer. The results revealed a discrepancy of ±9 mmHg in systolic values and ±7 mmHg in diastolic values between the two devices. The smallest systolic error observed was 0.4%, while the largest error reached 3%. Similarly, the smallest diastolic error was 2%, with the largest error recorded at 4.8%. The measurement errors, particularly in diastolic pressure, were influenced by the participants' fatigue, as the repeated measurements on the same arm led to slight arm movements during the process. The study demonstrated the successful transmission of measurement results to a smartphone, affirming the efficacy of the Blynk application. Additionally, the MPX5050GP sensor proved effective in detecting blood pressure. These findings highlight the potential of the developed digital sphygmomanometer as a reliable tool for blood pressure monitoring, promoting self-care and early detection of health issues.
41
Elabd, Rania H., Maged M. Darwish, Hazem H. Alshamekh, et al. "IOT Smart Helmet System for Delivery Rider Safety." Journal of Engineering Research and Reports 27, no. 6 (2025): 236–45. https://doi.org/10.9734/jerr/2025/v27i61541.
Full textAbstract:
This paper introduces Helmify, an innovative IoT-based smart helmet system meticulously designed to enhance the safety and operational efficiency of delivery drivers, particularly those serving small to medium-sized enterprises. The key methodological approach of Helmify lies in its holistic integration of real-time, sensor-based safety monitoring (MPU-6050 for fall detection, Force Sensitive Resistor (FSR) for helmet compliance) with GPS-enabled logistics management (via smartphone-based location tracking and order updates) and immediate alert propagation across a rider's dedicated Android mobile application and a centralized administrative web dashboard. The system architecture leverages the ESP32 microcontroller, integrating it with an MPU-6050 inertial measurement unit for fall detection, an FSR for helmet compliance monitoring, and Bluetooth Low Energy (BLE) for wireless communication with a rider's smartphone. Core functionalities include precise helmet wear detection, rapid fall detection alerts, and seamless integration with a custom-developed Android mobile application. These features are engineered to minimize accident risks and promote consistent helmet usage, thereby significantly improving driver safety. The Helmify system further incorporates real-time GPS tracking (via the rider's smartphone) for efficient driver location management and order status updates. This is integrated with a user-friendly mobile application enabling drivers to receive orders, navigate optimized routes using Google Maps API, and communicate with administrative staff. Administrators can monitor driver activities, track deliveries in real-time, and access performance data through a dedicated web dashboard, facilitating efficient management of delivery operations. The proposed solution addresses prevalent challenges in last-mile delivery logistics by combining safety monitoring, communication tools, and operational tracking within a single, scalable system. The implementation relies on robust IoT sensors, efficient BLE communication, and Firebase cloud services (Firestore and Authentication) for continuous data flow, secure storage, and remote management. Emphasis has been placed on an energy-efficient design for the helmet-mounted electronics to ensure prolonged operational duration. Helmify aims to establish a new benchmark for smart transportation safety, providing a cost-effective, scalable, and integrated solution. It seeks to reduce accidents, improve helmet compliance, and optimize delivery workflows, contributing to a safer, more reliable, and efficient logistics industry. Future enhancements may include advanced data analytics, predictive alerts, and broader IoT ecosystem integration.
42
Petryk, V. F., A. G. Protasov, R. M. Galagan, A. V. Muraviov, and I. I. Lysenko. "Smartphone-Based Automated Non-Destructive Testing Devices." Devices and Methods of Measurements 11, no. 4 (2020): 272–78. http://dx.doi.org/10.21122/2220-9506-2020-11-4-272-278.
Full textAbstract:
Currently, non-destructive testing is an interdisciplinary field of science and technology that serves to ensure the safe functioning of complex technical systems in the face of multifactorial risks. In this regard, there is a need to consider new information technologies based on intellectual perception, recognition technology, and general network integration. The purpose of this work was to develop an ultrasonic flaw detector, which uses a smartphone to process the test results, as well as transfer them directly to an powerful information processing center, or to a cloud storage to share operational information with specialists from anywhere in the world.The proposed flaw detector consists of a sensor unit and a smartphone. The exchange of information between the sensor and the smartphone takes place using wireless networks that use "bluetooth" technology. To ensure the operation of the smartphone in the ultrasonic flaw detector mode, the smartphone has software installed that runs in the Android operating system and implements the proposed algorithm of the device, and can serve as a repeater for processing data over a considerable distance (up to hundreds and thousands of kilometers) if it necessary.The experimental data comparative analysis of the developed device with the Einstein-II flaw detector from Modsonic (India) and the TS-2028H+ flaw detector from Tru-Test (New Zealand) showed that the proposed device is not inferior to them in terms of such characteristics as the range of measured thicknesses, the relative error in determining the depth defect and the object thickness. When measuring small thicknesses from 5 to 10 mm, the proposed device even surpasses them, providing a relative measurement error of the order of 1 %, while analogues give this error within 2–3 %.
43
Padinhakara, Mohammed Javed. "Smartphone-Enabled Vehicular Control: Architectural Frameworks and Technical Benefits of Connected Car Systems." European Journal of Computer Science and Information Technology 13, no. 42 (2025): 1–11. https://doi.org/10.37745/ejcsit.2013/vol13n42111.
Full textAbstract:
The technological frameworks enabling smartphone-based vehicle control within connected car ecosystems provide architectural components that facilitate remote vehicle access, diagnostic monitoring, and personalized user experiences. Secure cloud-based integration methodologies between mobile applications and vehicular systems incorporate authentication protocols that protect against unauthorized access while maintaining user convenience. Over-the-air update mechanisms create pathways for continuous feature enhancement without requiring physical dealer intervention, while IoT sensor networks enable predictive maintenance capabilities that significantly reduce vehicle downtime. Machine learning algorithms adapt vehicle settings based on usage patterns and environmental factors, enhancing operational efficiency and driver comfort. Additionally, the integration of V2X communication protocols demonstrates substantial improvements in navigational precision and safety through real-time data exchange with surrounding infrastructure and vehicles. Implementation challenges related to network dependency and latency issues necessitate engineering solutions that balance technical performance with evolving regulatory requirements in the automotive connectivity landscape.
44
Roux de Bézieux, Hector, James Bullard, Orville Kolterman, Michael Souza, and Fanny Perraudeau. "Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept Study." JMIR Formative Research 5, no. 3 (2021): e20175. http://dx.doi.org/10.2196/20175.
Full textAbstract:
Background Novel wearable biosensors, ubiquitous smartphone ownership, and telemedicine are converging to enable new paradigms of clinical research. A new generation of continuous glucose monitoring (CGM) devices provides access to clinical-grade measurement of interstitial glucose levels. Adoption of these sensors has become widespread for the management of type 1 diabetes and is accelerating in type 2 diabetes. In parallel, individuals are adopting health-related smartphone-based apps to monitor and manage care. Objective We conducted a proof-of-concept study to investigate the potential of collecting robust, annotated, real-time clinical study measures of glucose levels without clinic visits. Methods Self-administered meal-tolerance tests were conducted to assess the impact of a proprietary synbiotic medical food on glucose control in a 6-week, double-blind, placebo-controlled, 2×2 cross-over pilot study (n=6). The primary endpoint was incremental glucose measured using Abbott Freestyle Libre CGM devices associated with a smartphone app that provided a visual diet log. Results All subjects completed the study and mastered CGM device usage. Over 40 days, 3000 data points on average per subject were collected across three sensors. No adverse events were recorded, and subjects reported general satisfaction with sensor management, the study product, and the smartphone app, with an average self-reported satisfaction score of 8.25/10. Despite a lack of sufficient power to achieve statistical significance, we demonstrated that we can detect meaningful changes in the postprandial glucose response in real-world settings, pointing to the merits of larger studies in the future. Conclusions We have shown that CGM devices can provide a comprehensive picture of glucose control without clinic visits. CGM device usage in conjunction with our custom smartphone app can lower the participation burden for subjects while reducing study costs, and allows for robust integration of multiple valuable data types with glucose levels remotely. Trial Registration ClinicalTrials.gov NCT04424888; http://clinicaltrials.gov/ct2/show/NCT04424888.
45
Min Ryu, Soo, Hye Jin Kim, Jin Ho Lee, and Kentaro Yamagishi. "Real-Time Air Density Measurement with IoT Integration." Journal of Engineering, Technology, and Applied Science (JETAS) 5, no. 3 (2023): 99–105. http://dx.doi.org/10.36079/lamintang.jetas-0503.143.
Full textAbstract:
This paper presents the design and implementation of an IoT-based air density measurement system that integrates BMP180 and DHT22 sensors with an Arduino Uno microcontroller and ESP8266 Wi-Fi module. The system measures temperature, humidity, and atmospheric pressure to calculate air density in real-time, displaying the results on an LCD screen and transmitting the data to a smartphone app (Blynk) for remote monitoring. The goal of this research is to create a reliable, automated system capable of providing continuous air density measurements without the need for manual intervention. To evaluate the system’s accuracy, data were collected and compared with reference values from the Korea Meteorological Administration (KMA) for August 2023. The comparison revealed that the system produced air density measurements with an average error of less than 0.2%, demonstrating its high level of accuracy and reliability. The system is particularly suited for laboratory environments, where real-time and accurate air density measurements are essential. The use of IoT technology allows for remote data access and continuous monitoring, making the system convenient for various applications, including environmental monitoring and industrial settings where air density plays a crucial role. Future improvements could include sensor calibration enhancements and the integration of additional environmental parameters, such as CO2 levels or particulate matter, to broaden the system’s functionality. Overall, the IoT-based air density measurement system offers a cost-effective and scalable solution for real-time environmental data monitoring.
46
Mantri, Prathamesh, Ami Mehta, and Karan Kanchan. "Smartphone IMU Integration: Precision Boost for Sit-to-Stand Test with the Equilibrium Mobile Application." International Journal of Health Sciences and Research 14, no. 3 (2024): 284–94. http://dx.doi.org/10.52403/ijhsr.20240341.
Full textAbstract:
This study introduces an innovative approach that integrates the Inertial Measurement Unit (IMU) sensor of a smartphone for conducting the 5 Times Sit to Stand Test (5xSST) through the Equilibrium by Vayu Technology (EQ) mobile application. Engaging a sample of 16 participants, the research utilized the EQ mobile application for data acquisition during the 5xSST, in parallel with high-speed video recording of the participants executing the test. The video footage was then rigorously annotated by three independent experts. A comparative analysis of the data from the EQ mobile application and the annotated video revealed a significant level of precision, matching, and/or surpassing benchmarks set in existing research. Additionally, this study incorporates kinematic analysis for the 5 Times Sit to Stand Test (5xSST), providing a comprehensive evaluation of participants' movements. The Root Mean Square Error (RMSE) of this method stands at 3.26%, notably better than the results of traditional methods. The absolute error for the total duration of the 5xSST was recorded at 0.16 ± 0.1 seconds, which is consistent with the ranges reported in the literature. Additionally, a percentage error of 1.3 ± 0.84% highlights the accuracy and reliability of this smartphone-based approach. This study marks a significant advancement in functional mobility assessments, leveraging mobile technology to achieve greater precision and methodological rigor, thereby contributing a valuable perspective to the domain of health assessments. Key words: Inertial Measurement Unit (IMU),5 Times Sit to Stand Test (5xSST), Equilibrium (EQ) mobile application, Kinematic analysis, Functional mobility assessments, Smartphone-based health assessment, Precision and accuracy.
47
Kumar, Santosh. "What is driving the TSV business: Market & Technology Trends." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2019, DPC (2019): 000808–33. http://dx.doi.org/10.4071/2380-4491-2019-dpc-presentation_wp1_060.
Full textAbstract:
TSV interconnect based 3D/2.5D packaging has gained significant attention since its introduction in FPGA (for die partitioning) and HBM integrated GPU module (for gaming application). The performance potential offered by this technology is unequalled by any other packaging platform today. High-end applications like deep learning, datacenter networking, AR/VR, and autonomous driving are becoming real, thereby pushing the limits of other current packaging platforms. Fueled by increasing bandwidth needs for moving data in cloud-computing and supercomputing applications, performance-driven markets have adopted 3D stacked technologies in a row. Imaging, as the first market adopter of 3D integration, is propelling the market with an increasing number of sensors in smartphones and tablets, including 3D imaging. TSV-based products can be classified in three ranges: low, middle, and high-end. The middle and high-end product markets like CMOS image sensor, memory cube, and interposer are based on a via-middle process. In low-end products, we can also find TSV based on via-middle (i.e. in Apple's fingerprint sensor), but for cost reasons the MEMS industry is using essentially a via-last process, which is cheaper than a via-middle process. TSV's penetration rate in low-end products will remain stable, with the main source of growth due to RF filters in smartphone front-end modules, which keep increasing in order to support the different frequency bands used in 5G mobile communications protocol. This presentation will discuss about the market and technology trends of the TSV based 3D/2.5D packaging.
48
Hanafi, Muhammad, and Almasri. "Design and Implementation of an Arduino-Based Bluetooth-Controlled Shopping Trolley with Ultrasonic Sensor Integration." Journal of Hypermedia & Technology-Enhanced Learning (J-HyTEL) 2, no. 3 (2024): 320–37. http://dx.doi.org/10.58536/j-hytel.v2i3.148.
Full textAbstract:
Innovation in retail technology continues to evolve, aiming to enhance the shopping experience, particularly for customers with physical limitations. This research focuses on the design and implementation of an automated shopping cart system based on Arduino, controlled via Bluetooth using a smartphone. The system utilizes a Bluetooth HC-05 module for wireless communication, an ultrasonic HC-SR04 sensor for obstacle detection, and a motor driver to ensure smooth cart movement. The control application is developed using MIT App Inventor, offering navigation features such as forward, backward, left, and right turns. The ultrasonic sensor acts as a safety mechanism, detecting obstacles within 15 cm and providing warnings via a buzzer to prevent collisions. Testing results demonstrate high responsiveness and stable control over a distance of up to 10 meters. The findings of this study indicate that the automated shopping cart significantly improves convenience and accessibility, especially for customers with physical limitations, while ensuring a safer and more efficient shopping experience in supermarkets. This innovation presents a significant contribution to enhancing accessibility and operational efficiency in the retail sector.
49
Tamimi, Rami, and Charles Toth. "Experiments with Combining LiDAR and Camera Data Acquired by UAS and Smartphone to Support Mapping." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 619–27. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-619-2024.
Full textAbstract:
Abstract. Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy.
50
Naseer, Aisha, Ali Raza, Hadeeqa Afzal, et al. "Human pose estimation in physiotherapy fitness exercise correction using novel transfer learning approach." PeerJ Computer Science 11 (April 29, 2025): e2854. https://doi.org/10.7717/peerj-cs.2854.
Full textAbstract:
Objective To introduce and evaluate an efficient neural network approach for human pose estimation and correction during physical therapy exercises using wearable sensor data. Methods We leveraged benchmark data consisting of 276,625 records from wearable inertial and magnetic sensors. A novel method termed Random Forest Long Short-Term Memory (RFL), which integrates long short-term memory and Random Forest neural networks, was implemented for transfer feature engineering. The smartphone sensor data was used to generate new temporal and probabilistic features. These features were then utilized in machine learning methods to classify physical therapy exercises. Rigorous experiments, including k-fold validation and hyperparameter optimization, were conducted to validate the performance of the RFL approach. Results The RFL approach demonstrated superior performance, achieving a remarkable 99% accuracy with the Random Forest method. The rigorous experiments confirmed the efficacy and reliability of the method in classifying physical therapy exercises. Conclusions The proposed RFL method introduces a novel feature generation approach enhancing the accuracy of physical therapy exercise classification and correction. This innovative integration not only improves rehabilitation monitoring but also paves the way for more adaptive and intelligent physiotherapy assistance systems. By leveraging sensor data and advanced machine learning techniques, it has the potential to mitigate risks associated with disabilities and major diseases, thereby offering a feasible alternative to frequent clinic visits for consistent therapist guidance.