Journal articles: 'Smartwatch-android'

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Putra, Roni Anggara, Abdul Fadlil, and Imam Riadi. "Forensik Mobile Pada Smartwatch Berbasis Android." Jurnal Rekayasa Teknologi Informasi (JURTI) 1, no. 1 (June 8, 2017): 41. http://dx.doi.org/10.30872/jurti.v1i1.638.

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Perkembangan teknologi pada saat ini berkembang sangat pesat. Salah satu contoh berkembangnya alat telekomunikasi yang dipadukan dengan jam tangan yang dikenal sebagai smartwatch. Perkembangan smartwatch telah menyamai kemampuaan yang ada di smatphone, sehingga tidak menutup kemungkinan smartwatch dapat digunakan sebagai alat tindak kejahatan. Hal ini merupakan tantangan bagi IT forensik dan penegak hukum untuk melakukan penyelidikan terhadap smartwatch dari seseorang yang melakukan kejahatan dijadikan tersangka dari sebuah kasus. Caranya adalah dengan menerapkan pengembangan metode-metode forensic yang ada, sehingga dari hasil yang didapatkan dari metode forensic yang dilakukan diharapakan menjadi hasil yang berguna bagi IT forensic dan penegak hukum.

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Yundra, Eppy, Arif Widodo, Pradini Puspitaningayu, and Unit Three Kartini. "Wireless body area network's application for motion detection based on Android smartwatch." MATEC Web of Conferences 197 (2018): 11020. http://dx.doi.org/10.1051/matecconf/201819711020.

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Short-range, wireless communications in the vicinity of, or inside, a human body (but not limited to humans) are specified in this standard. It uses existing industrial scientific medical (ISM) bands as well as frequency bands approved by national medical and/or regulatory authorities. These sensors provide accurate and reliable information about the situation or activity occurring at any time. This high responsiveness of these sensors has made lives easy and safe. This article will perform an investigation on the WBAN's application for motion detection based on android smartwatch. The main problem is how to the sensors that attached on the human body can send of data via Bluetooth and read the motion and orientation of the body and display in the graph form. However, if the sensors do not match, the result of output display inappropriate. This article proposed method by using accelerometer and gyroscope sensors which are integrated with smartwatch to overcome the main problem. This application will display results in the form of numeric values and then convert in graph form. These results have proven that WBAN's application for motion sensing can be used for detection of motion and orientation via Bluetooth based on android smartwatch.

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Amor, James D., and Christopher J. James. "Validation of a Commercial Android Smartwatch as an Activity Monitoring Platform." IEEE Journal of Biomedical and Health Informatics 22, no. 4 (July 2018): 968–78. http://dx.doi.org/10.1109/jbhi.2017.2732678.

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Indrayana, I. N. E., P. Sutawinaya, N. M. W. D. Pratiwi, P. M. Prihatini, and S. A. Asri. "Android-Based Child Monitoring Application Using A Smartwatch and Geofence Service." Journal of Physics: Conference Series 1803 (February 2021): 012024. http://dx.doi.org/10.1088/1742-6596/1803/1/012024.

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Bedjo Utomo, Syaifudin Syaifudin, Endang Dian Setioningsih, Torib Hamzah, and Parameswaran Parameswaran. "Oximeter and BPM on Smartwatch Device Using Mit-App Android with Abnormality Alarm." Journal of Electronics, Electromedical Engineering, and Medical Informatics 3, no. 2 (July 19, 2021): 85–92. http://dx.doi.org/10.35882/jeeemi.v3i2.4.

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Monitoring is an activity that is carried out continuously. Healthy condition is a parameter that is needed in life, one of the important parameters is the measurement of oxygen saturation in the blood and heart rate. The purpose of this research is to develop a Smartwatch SpO2 device and BPM sensor that is connected to WIFI using the Android Platform instead of using an LCD for parameter reading. This module design method uses the MAX30100 sensor to display the SpO2 and BPM values displayed on the OLED. Data processing is carried out using ATMEGA 328P programming and then displayed in the Android-based Mit-app application. The results show the average error for the SPO2 value is 0.868 % and the standard deviation is 0.170 %, while the BPM value has an average error of 0.56 % and a standard deviation of 0.30%. From the results of the comparison data analysis, the largest error was 1.03% and the smallest was 0.62% for Spo2 ml/hour with an accuracy of 0.05 (0.57%) with a precision value of 0.08 at the selection speed of 50 ml/hour. From the results above, it can be concluded that the data can be displayed on OLED using the Mit-app Android application with an error rate accuracy of 0.57%. From the results of this research design, it is hoped that it can facilitate the diagnosis of the condition of patients and health nurses

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Kosanovic, Milos, and Slavimir Stosovic. "The concept for the “Smart Home” controlled by a smartwatch." Facta universitatis - series: Electronics and Energetics 31, no. 3 (2018): 389–400. http://dx.doi.org/10.2298/fuee1803389k.

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In this paper a ?Smart Home? solution is proposed in which power plugs in a remote room can be controlled by a smartwatch, an Android mobile device or a PHP web app. Communication between these devices takes place in real time via server using Node.js technology. An electrical circuit for determining current and voltage on the plugs via Arduino Wi-Fi module sends the measured values to the server, based on which the electrical energy consumption in each time interval can be determined. All the measured values are stored in MySQL database and used for creation of appropriate reports. Smartwatch app enables remote plugging and unplugging. In addition, the setting of limits for electrical energy consumption on each plug is enabled, as well as the power of the consumption device that can be plugged. Exceeding of the allowed values leads to the automatic unplugging.

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Do, Quang, Ben Martini, and Kim-Kwang Raymond Choo. "Is the data on your wearable device secure? An Android Wear smartwatch case study." Software: Practice and Experience 47, no. 3 (May 18, 2016): 391–403. http://dx.doi.org/10.1002/spe.2414.

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Mezari, Antigoni, and Ilias Maglogiannis. "An Easily Customized Gesture Recognizer for Assisted Living Using Commodity Mobile Devices." Journal of Healthcare Engineering 2018 (July 19, 2018): 1–12. http://dx.doi.org/10.1155/2018/3180652.

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Automatic gesture recognition is an important field in the area of human-computer interaction. Until recently, the main approach to gesture recognition was based mainly on real time video processing. The objective of this work is to propose the utilization of commodity smartwatches for such purpose. Smartwatches embed accelerometer sensors, and they are endowed with wireless communication capabilities (primarily Bluetooth), so as to connect with mobile phones on which gesture recognition algorithms may be executed. The algorithmic approach proposed in this paper accepts as the input readings from the smartwatch accelerometer sensors and processes them on the mobile phone. As a case study, the gesture recognition application was developed for Android devices and the Pebble smartwatch. This application allows the user to define the set of gestures and to train the system to recognize them. Three alternative methodologies were implemented and evaluated using a set of six 3-D natural gestures. All the reported results are quite satisfactory, while the method based on SAX (Symbolic Aggregate approXimation) was proven the most efficient.

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Mauldin, Taylor, Marc Canby, Vangelis Metsis, Anne Ngu, and Coralys Rivera. "SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning." Sensors 18, no. 10 (October 9, 2018): 3363. http://dx.doi.org/10.3390/s18103363.

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This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing.

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Jusoh, Rosmalissa, Ahmad Firdaus, Shahid Anwar, Mohd Zamri Osman, Mohd Faaizie Darmawan, and Mohd Faizal Ab Razak. "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)." PeerJ Computer Science 7 (June 11, 2021): e522. http://dx.doi.org/10.7717/peerj-cs.522.

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Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony. Notably, the employment of OS leads to a rapid increase in the number of Android users. However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. Although practitioners conduct intrusion detection analyses, such as static analysis, there is an inadequate number of review articles discussing the research efforts on this type of analysis. Therefore, this study discusses the articles published from 2009 until 2019 and analyses the steps in the static analysis (reverse engineer, features, and classification) with taxonomy. Following that, the research issue in static analysis is also highlighted. Overall, this study serves as the guidance for novice security practitioners and expert researchers in the proposal of novel research to detect malware through static analysis.

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I Nyoman Eddy Indrayana, I Putu Sutawinaya, Ni Made Wirasyanti Dwi Pratiwi, and Kadek Amerta Yasa. "APLIKASI PEMBATASAN AREA VIRTUAL (GEOFENCE) UNTUK PEMANTAUAN AKTIFITAS ANAK-ANAK MENGGUNAKAN SMARTPHONE DAN SMART WATCH." JURNAL FASILKOM 10, no. 3 (December 11, 2020): 177–83. http://dx.doi.org/10.37859/jf.v10i3.2325.

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Pemantauan pergerakan anak-anak sangat dibutuhkan oleh orang tua untuk menghindari hal hal yang tidak diinginkan. Teknologi GPS dapat dimanfaatkan untuk melakukan pengawasan melalui smartphone yang dikombinasikan dengan pembatasan area secara virtual(geofence) untuk meningkatkan pengawasan pada saat orang tua tidak berada di dekat anak-anaknya. Pembatasan area secara virtual ini akan membantu memberikan sinyal otomatis kepada orang tua pada saat anak-anak melakukan pergerakan diluar area yang diijinkan. Aplikasi ini dibangun menggunakan metode Unified System Development Process(USDP) dan menerapkan rumus Haversine untuk menghitung jarak antara 2 posisi di bumi. Rumus Haversine ini digunakan dalam proses mendeteksi apakah suatu objek berada pada area yang diijinkan ataukah tidak. Pembatasan area virtual dalam aplikasi ini menggunakan bentuk lingkaran. Aplikasi ini membutuhkan 2 aktor yaitu aktor orang tua yang bertindak sebagai pemantau dan aktor anak yang bertindak sebagai objek yang dipantau. Orang tua menggunakan smartphone android untuk melihat pergerakan anak melalui google map dan sekaligus untuk mengatur geofence si anak sehingga lebih mudah melakukan pemantauan. Sedangkan si anak menggunakan smartwatch untuk mengirimkan koordinat latitude dan longitude posisinya ke server database firebase. Perangkat smartwatch yang digunakan si anak ini, lebih ringan dan lebih praktis karena dipakai di tangan

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I Nyoman Eddy Indrayana, I Putu Sutawinaya, Ni Made Wirasyanti Dwi Pratiwi, and Kadek Amerta Yasa. "APLIKASI PEMBATASAN AREA VIRTUAL (GEOFENCE) UNTUK PEMANTAUAN AKTIFITAS ANAK-ANAK MENGGUNAKAN SMARTPHONE DAN SMART WATCH." JURNAL FASILKOM 10, no. 3 (December 11, 2020): 177–83. http://dx.doi.org/10.37859/jf.v10i3.2325.

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Pemantauan pergerakan anak-anak sangat dibutuhkan oleh orang tua untuk menghindari hal hal yang tidak diinginkan. Teknologi GPS dapat dimanfaatkan untuk melakukan pengawasan melalui smartphone yang dikombinasikan dengan pembatasan area secara virtual(geofence) untuk meningkatkan pengawasan pada saat orang tua tidak berada di dekat anak-anaknya. Pembatasan area secara virtual ini akan membantu memberikan sinyal otomatis kepada orang tua pada saat anak-anak melakukan pergerakan diluar area yang diijinkan. Aplikasi ini dibangun menggunakan metode Unified System Development Process(USDP) dan menerapkan rumus Haversine untuk menghitung jarak antara 2 posisi di bumi. Rumus Haversine ini digunakan dalam proses mendeteksi apakah suatu objek berada pada area yang diijinkan ataukah tidak. Pembatasan area virtual dalam aplikasi ini menggunakan bentuk lingkaran. Aplikasi ini membutuhkan 2 aktor yaitu aktor orang tua yang bertindak sebagai pemantau dan aktor anak yang bertindak sebagai objek yang dipantau. Orang tua menggunakan smartphone android untuk melihat pergerakan anak melalui google map dan sekaligus untuk mengatur geofence si anak sehingga lebih mudah melakukan pemantauan. Sedangkan si anak menggunakan smartwatch untuk mengirimkan koordinat latitude dan longitude posisinya ke server database firebase. Perangkat smartwatch yang digunakan si anak ini, lebih ringan dan lebih praktis karena dipakai di tangan

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Gilmore, James N. "From Ticks and Tocks to Budges and Nudges." Television & New Media 18, no. 3 (August 1, 2016): 189–202. http://dx.doi.org/10.1177/1527476416658962.

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This article uses the emergence of smartwatch models from 2012 to 2015—including the Pebble Watch, Android Wear, and Apple Watch—to explore the relationship between “instants” and “information.” Through a focus on the ways in which smartwatches notify, buzz, and otherwise touch the skin, this article develops the idea of the “haptic instant” as a key feature of this technology. The haptic instant, in calling attention to the delivery of news alerts, e-mails, personal communication, and other notifications to the wearer’s wrist, is part of the larger formation of bodies capable of living in a culture increasingly reliant on computer information systems for the management of daily life.

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PEKTAŞ, Ömer, Murat KÖSEOĞLU, Miroslav MUZNY, Gunnar HARTVİGSEN, and Eirik ÅRSAND. "Design of an Android Wear Smartwatch Application as a Wearable Interface to the Diabetes Diary Application." Academic Platform Journal of Engineering and Science 9, no. 1 (January 29, 2021): 126–33. http://dx.doi.org/10.21541/apjes.660490.

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Narváez, Pedro, José Manjarrés, Winston Percybrooks, Mauricio Pardo, and Maria Calle. "Assessing the Level of Physical Activity in the Workplace." International Journal of Interdisciplinary Telecommunications and Networking 11, no. 1 (January 2019): 44–56. http://dx.doi.org/10.4018/ijitn.2019010104.

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Occupational hygiene requires evaluation of different risk sources in the workplace. The level of physical workload may create stress, fatigue and injuries. Therefore, activity monitoring provides valuable information for companies in assessing and solving possible hazards in the workplace. The article presents a system using wearable technology to monitor and evaluate physical workload with in situ measurements. The system uses a smartwatch and a mobile application for Android phones. During workload monitoring, the application displays physiologic variables such as heart rate, calories, body temperature, galvanic skin response and number of steps. Additionally, the system computes absolute and relative cardiac cost, and Frimat coefficients. Tests were performed on 10 individuals from a janitor staff (5 men and 5 women), monitoring every task during their most demanding hour. Results agree with the type of activity developed in different intervals, showing light and very light workload for different tasks in all workers.

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Herrera-Alcántara, Oscar, Ari Barrera-Animas, Miguel González-Mendoza, and Félix Castro-Espinoza. "Monitoring Student Activities with Smartwatches: On the Academic Performance Enhancement." Sensors 19, no. 7 (April 3, 2019): 1605. http://dx.doi.org/10.3390/s19071605.

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Motivated by the importance of studying the relationship between habits of students and their academic performance, daily activities of undergraduate participants have been tracked with smartwatches and smartphones. Smartwatches collect data together with an Android application that interacts with the users who provide the labeling of their own activities. The tracked activities include eating, running, sleeping, classroom-session, exam, job, homework, transportation, watching TV-Series, and reading. The collected data were stored in a server for activity recognition with supervised machine learning algorithms. The methodology for the concept proof includes the extraction of features with the discrete wavelet transform from gyroscope and accelerometer signals to improve the classification accuracy. The results of activity recognition with Random Forest were satisfactory (86.9%) and support the relationship between smartwatch sensor signals and daily-living activities of students which opens the possibility for developing future experiments with automatic activity-labeling, and so forth to facilitate activity pattern recognition to propose a recommendation system to enhance the academic performance of each student.

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Gatsios, Dimitris, Angelo Antonini, Giovanni Gentile, Andrea Marcante, Clelia Pellicano, Lucia Macchiusi, Francesca Assogna, et al. "Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial." JMIR mHealth and uHealth 8, no. 6 (June 29, 2020): e16414. http://dx.doi.org/10.2196/16414.

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Background Mobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely. Objective We explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities. Methods PD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson’s Disease Rating Scale estimations by clinicians were explored. Results Of the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers’ burden. Motor Aspects of Experiences of Daily Living and patients’ self-rated health status also influence the system’s use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor. Conclusions We found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care. Trial Registration ISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879 International Registered Report Identifier (IRRID) RR2-10.1186/s13063-018-2767-4

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Savi, Daniela, Luigi Graziano, Barbara Giordani, Stefano Schiavetto, Corrado De Vito, Giuseppe Migliara, Nicholas J. Simmonds, Paolo Palange, and J. Stuart Elborn. "New strategies of physical activity assessment in cystic fibrosis: a pilot study." BMC Pulmonary Medicine 20, no. 1 (October 30, 2020). http://dx.doi.org/10.1186/s12890-020-01313-5.

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Abstract Background Regular physical activity (PA) is a valued part of cystic fibrosis (CF) care. Although the accelerometer, SenseWear Armband (SWA), accurately measures habitual PA in CF, it is mostly used for research purposes. For the first time, we analyzed different methods of measuring PA in daily life by the use of smartphones and other electronic devices such as smartwatch and Fitbit. Methods Twenty-four stable adults with CF (mean age 37.5 ± 11.5SD yrs.; FEV1 58 ± 19% predicted, BMI 22.9 ± 3.2) were studied. Daily PA was monitored for seven consecutive days. All patients wore the accelerometer SWA and at the same time they monitored PA with the electronic device they used routinely. They were allocated into one of four arms according to their device: Smartwatch, Fitbit, Android smartphones and iOS smartphones. PA related measurements included: duration of PA, energy expenditure, number of steps. Results There was a good agreement between SWA and Fitbit for number of steps (p = 0.605) and energy expenditure (p = 0.143). iOS smartphones were similar to SWA in monitoring the number of steps (p = 0.911). Significant differences were found between SWA and both Smartwatch and Android smartphones. Conclusions Fitbit and iOS smartphones seem to be a valuable approach to monitor daily PA. They provide a good performance to measure step number compared to SWA.

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Turner, Colton J., Barbara S. Chaparro, and Jibo He. "Typing on a Smartwatch While Mobile: A Comparison of Input Methods." Human Factors: The Journal of the Human Factors and Ergonomics Society, February 7, 2020, 001872081989129. http://dx.doi.org/10.1177/0018720819891291.

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Objective The user experience of typing on a smartwatch was evaluated with three unique input methods (tap, trace, and handwriting) while standing and while walking. Background Despite widespread development within the technology industry, smartwatches have had a relatively slow adoption worldwide compared to smartphones. One limiting factor of smartwatches has been the lack of an efficient means of text entry. The 2017 release of Android Wear addressed this issue by providing support for native text entry (i.e., tap, trace, and handwriting). Determining how user performance and subjective ratings compare across these input methods is essential to understanding their contribution to smartwatch user experience. Method Twenty college-age individuals typed phrases using tap, trace, and handwriting input on a smartwatch in three different mobility scenarios (standing, walking a simple course, walking a complex course). Results Participants typed faster with trace (30 words per minute; WPM) than with tap (20 WPM) and handwriting (18 WPM), regardless of mobility. Trace also outperformed tap and handwriting across all subjective metrics, regardless of mobility. Conclusion Trace input appears to be especially well suited for typing on a smartwatch as it was found to be objectively and subjectively superior to tap and handwriting regardless of user mobility. Objectively, typing speeds with trace are shown to be nearly two times faster than most alternative input methods described in the literature. Application Results suggest smartwatch manufacturers should include QWERTY keyboards with trace input as a standard feature in order to provide the best overall typing experience for their users.

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"Healthcare Smartwatch for Monitoring Elderly." International Journal of Innovative Technology and Exploring Engineering 9, no. 2 (December 10, 2019): 737–41. http://dx.doi.org/10.35940/ijitee.b6834.129219.

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According to the data provided by the Census in 2011, around 15 million elderly live alone in India. An NGO from Delhi named Agewell Foundation performed a survey on 10,000 elderly individuals and concluded that every fourth elderly that is 23.44% of the people who responded were living alone [1]. They also reported that around 88.5% felt the need of healthcare monitoring to ensure their independence. A lot of research work is going on to come up with a perfect solution to solve this problem and there are many computing devices which have been invented which solve the problem partially faced by elderly people. But none of them provide a solution which is convenient to use and which covers all aspects of the problem ranging from medicine reminders for elderly to proper maintaining of health records and it’s timely reporting to the doctor and family members even during emergencies such as a cardiac arrest. This paper proposes a neat solution to help monitor the daily reports of the elderly by automatically sending the data [12] (real-time [10][15] heart rate and current location measured by smartwatch; blood pressure measured by machine; sugar level measured by machine) to their family via android application and also provide 1st degree detection of cardiac arrest and prediction of heart problems up to some accuracy. It will provide medication [9] reminders to the elderly by three vibrations after an interval of 5 minutes during one course of medication and will also provide assistance during emergencies by sending an SOS call to the family or doctor. The smartwatch uses microcontroller ATmega328, SIM800L GSM module [11][14], RSI 0.96 inch 4 Pin OLED display module, HC-06 Bluetooth module, small LiPo battery, mini 1020 flat vibration motor, pulse sensor, GPS NEO 6M module [11], micro USB, button. This solution will not only help the elderly in rightly monitoring [8] their daily data [12] but also inform their family with their daily reports and any unusual changes. The smart watch acts as a care taker of the elderly people and it will be useful to the elderly who stay alone most of the time and it can even be used by others for proper maintaining of their health records, for medicine reminders and assistance during emergency conditions.

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Journal articles on the topic 'Smartwatch-android'

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