To see the other types of publications on this topic, follow the link: Touchless machine.
Journal articles on the topic 'Touchless machine'
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 'Touchless machine.'
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
Shahakar, Y. D. "Smart Technology for Hygeia Hub." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–7. https://doi.org/10.55041/ijsrem41683.
Full textAbstract:
This project presents, intelligent touch- free restroom system designed to promote hygiene, reduce water waste, and enhance user experience. The system integrates advanced sensors, actuators, and automation technologies to create a seamless, hands-free environment. Key features include touchless faucets, automatic soap dispensers, touchless toilets, and intelligent hand dryers. The system's control and automation module utilize machine learning algorithms to optimize water usage, detect maintenance needs, and ensure continuous operation. The system's touchless interface minimizes the risk of germ transmission, providing a healthier environment for users. Water efficiency is also a key feature, as the system optimizes water usage, reducing waste and conserving this precious resource. The intelligent automation module detects maintenance needs and ensures continuous operation, reducing downtime and increasing overall system efficiency. Keywords- Ardunio, Sensor-based restroom, Hands-free, solenoid valve, Smart restroom, Hygienic.
2
Aryan, Gupta, Narula Rachna, Garg Parth, Joshi Diksha, and Upadhyay Navneet. "Touchless Operations Using Hand Gestures Detection." Recent Innovations in Wireless Network Security 5, no. 3 (2023): 31–40. https://doi.org/10.5281/zenodo.8285242.
Full textAbstract:
<em>Humans have only recently begun using hand gestures to interact with computers. The integration of the real and digital worlds is the aim of gesture recognition. It is considerably simpler to convey our intentions and ideas to the computer via hand gestures. A simple and efficient touchless method of interacting with computer systems is through hand gestures. However, the limited end-user adoption of hand gesture-based systems is mostly caused by the significant technical challenges involved in successfully identifying in-air movements. Image recognition is one of the many ways that a computer may identify a hand gesture. The ability to recognise human movements is made possible by the deployment of a convolutional neural network (CNN). In this research, we build a straightforward hand tracking technique to operate a Robot Operating System (ROS) based surveillance car with socket programming using Google MediaPipe, a Machine Learning (ML) pipeline that integrates Palm Detection and Hand Landmark Models. In the investigation, steering speed and direction of a ROS automobile are controlled. Vehicles for surveillance that can be operated using hand gestures may help to enhance security measures.</em> <strong><em> </em></strong>
3
Ferri, Llopis, Moreno, Ibañez Civera, and Garcia-Breijo. "A Wearable Textile 3D Gesture Recognition Sensor Based on Screen-Printing Technology." Sensors 19, no. 23 (2019): 5068. http://dx.doi.org/10.3390/s19235068.
Full textAbstract:
Research has developed various solutions in order for computers to recognize hand gestures in the context of human machine interface (HMI). The design of a successful hand gesture recognition system must address functionality and usability. The gesture recognition market has evolved from touchpads to touchless sensors, which do not need direct contact. Their application in textiles ranges from the field of medical environments to smart home applications and the automotive industry. In this paper, a textile capacitive touchless sensor has been developed by using screen-printing technology. Two different designs were developed to obtain the best configuration, obtaining good results in both cases. Finally, as a real application, a complete solution of the sensor with wireless communications is presented to be used as an interface for a mobile phone.
4
Siddharth, Swami, Mohan Joshi Lalit, Ismail Iqbal Mohammed, et al. "High body temperature detection solution through touchless machine for health monitoring." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 166–71. https://doi.org/10.11591/ijai.v14.i1.pp166-171.
Full textAbstract:
The demand for reliable health monitoring systems has surged in today's health-conscious society. Body temperature monitoring is crucial for preserving health and preventing infectious disease outbreaks. In this study an Arduino uno hardware board with a touchless temperature sensor is proposed to detect elevated body temperature, indicating fever and early signs of illness. The system prioritizes real-time health surveillance, accessibility, and usability, blending seamlessly with normal life. Arduino's versatility allows the system to function covertly, uphold privacy and autonomy, and foster wellbeing. The goal is to highlight the system's ability to function covertly, uphold privacy and autonomy, and foster wellbeing. This technology exemplifies the synergy between personal wellness and contemporary technologies, offering a useful and adaptable fever detection solution for various contexts, including homes and public areas.
5
Akshay Velapure. "Performance Evaluation of Touchless Fingerprint Recognition: A Comparative Study of SVM VS Decision Trees." Journal of Information Systems Engineering and Management 10, no. 25s (2025): 532–40. https://doi.org/10.52783/jisem.v10i25s.4087.
Full textAbstract:
Since the encounter of COVID-19 pandemic, different aspects of daily life in early 2020 had got considerably impacted. To control the rate of newly introduced viral infections a range of various measures were recommended worldwide such as the use of facial masks, face shield, enhanced hand hygiene practices etc helped to decrease the spread of pathogens in social gatherings. Nevertheless, these specific measures were creating difficulties in ensuring the reliability of biometric recognition methods, such as voice, facial, and hand-based biometrics. To avoid problems associated with contact/touch – based Biometrics, in this work we have designed an algorithm for touchless fingerprint recognition using HOG features and Machine Learning classifiers. Performance of recognition is evaluated for SVM vs Decision Tree algorithms. The integration of "HOG features with SVM" proves to be more effective in Touchless Fingerprint Recognition domain.
6
Swami, Siddharth, Lalit Mohan Joshi, Mohammed Ismail Iqbal, et al. "High body temperature detection solution through touchless machine for health monitoring." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 166. http://dx.doi.org/10.11591/ijai.v14.i1.pp166-171.
Full textAbstract:
<span lang="EN-US">The demand for reliable health monitoring systems has surged in today's health-conscious society. Body temperature monitoring is crucial for preserving health and preventing infectious disease outbreaks. In this study an Arduino uno hardware board with a touchless temperature sensor is proposed to detect elevated body temperature, indicating fever and early signs of illness. The system prioritizes real-time health surveillance, accessibility, and usability, blending seamlessly with normal life. Arduino's versatility allows the system to function covertly, uphold privacy and autonomy, and foster wellbeing. The goal is to highlight the system's ability to function covertly, uphold privacy and autonomy, and foster wellbeing. This technology exemplifies the synergy between personal wellness and contemporary technologies, offering a useful and adaptable fever detection solution for various contexts, including homes and public areas.</span>
7
Makarov, Denys. "Compliant Magnetic Field Sensor Technologies." Engineering Proceedings 6, no. 1 (2021): 8. http://dx.doi.org/10.3390/i3s2021dresden-10066.
Full textAbstract:
We review the recent progress in the field of shapeable magnetoelectronics allowing the realization of mechanically imperceptible electronic skins, which enable perception of the geomagnetic field (e-skin compasses), featuring sensitivities down to ultra-small fields of sub-50 nT. We demonstrate that e-skin compasses allow humans to orient with respect to Earth’s magnetic field ubiquitously. The biomagnetic orientation enables novel interactive devices for virtual and augmented reality applications, which is showcased by realizing touchless control of virtual units in a game engine using omnidirectional magnetosensitive skins. This concept is further extended by demonstrating a compliant magnetic microelectromechanical platform (m-MEMS), which is able to transduce both tactile (via mechanical pressure) and touchless (via magnetic field) stimulations simultaneously and discriminate them in real time. These devices are crucial for interactive electronics and human–machine interfaces, but also for the realization of smart soft robotics with highly compliant integrated feedback systems including in medicine for physicians and surgeons.
8
Lecca, Michela, Massimo Gottardi, Elisabetta Farella, and Bojan Milosevic. "Always-on low-power optical system for skin-based touchless machine control." Journal of the Optical Society of America A 33, no. 6 (2016): 1015. http://dx.doi.org/10.1364/josaa.33.001015.
Full text
9
Uppal, Mudita, Harsha Chauhan, Deepali Gupta, and Jayant Uppal. "A Framework of Smart Dispensing Unit for Optimizing Sanitizer Quantity Using Iot Schematics." ECS Transactions 107, no. 1 (2022): 8141–46. http://dx.doi.org/10.1149/10701.8141ecst.
Full textAbstract:
Nowadays, technology is helping everyone to cover their basic hygiene needs. During the COVID-19 pandemic, every person has started taking precautions, such as hand sanitization, wearing of masks, and social distancing. In this paper, the authors are focusing on hand sanitization. Latest sanitizer machines dispense sanitizer automatically because of touchless technology, but excessive use of sanitizer is harmful to the human skin. To address this issue, a smart sanitizer dispenser based on the Internet of Things (IoT) and Machine Learning (ML) has been proposed to fight against COVID-19. The proposed solution captures the image of hands and based on the size of hands and the number of germs present on the hands, the machine will dispense the sanitizer. In this way, the damage to human skin caused due to the excessive use of sanitizer can be reduced and the amount of sanitizer can also be saved.
10
J Jagadeesan, M. Azhagiri, and M. Gowtham Sethupathi. "Touchless ATM Using Augmented Reality Using TOTP Haar Cascade Algorithm." International Journal of Soft Computing and Engineering 15, no. 1 (2025): 5–8. https://doi.org/10.35940/ijsce.f3506.15010325.
Full textAbstract:
Touchless ATMs, a new technology, offer a contactfree, hygienic, and convenient financial transaction experience. This innovative solution uses Augmented Reality (AR), Timebased One-Time Passwords (TOTP), and the HAAR Cascade Algorithm to create an interactive virtual interface, reducing physical contact and enhancing transaction security. The system uses a dual-layered authentication mechanism, utilizing facial recognition and time-based, one-time passwords (TOTP) to validate user identities and generate dynamic, session-specific codes. Financial institutions can deploy this system to upgrade their ATM networks, catering to diverse user demographics. Challenges include developing robust gesture recognition models, ensuring low latency in AR interactions, and integrating these advanced technologies into existing ATM infrastructures. However, advances in hardware and software, coupled with the decreasing cost of AR and machine learning technologies, make this solution viable and scalable.
11
Makushko, Pavlo, Eduardo Sergio Oliveros Mata, Gilbert Santiago Cañón Bermúdez, et al. "Flexible Magnetoreceptor with Tunable Intrinsic Logic for On‐Skin Touchless Human‐Machine Interfaces." Advanced Functional Materials 31, no. 25 (2021): 2101089. http://dx.doi.org/10.1002/adfm.202101089.
Full text
12
Effiong, Otobong J., Akaninyene B. Obot, Kingsley M. Udofia, and Kufre M. Udofia. "Optimizing Touchless Fingerprint Identification: A Machine Learning Approach to Modelling and Performance Evaluation." Journal of Engineering Research and Reports 26, no. 10 (2024): 186–98. http://dx.doi.org/10.9734/jerr/2024/v26i101298.
Full textAbstract:
This paper explored the modelling and performance analysis of a smartphone-based fingerprint identification system using Convolutional Neural Networks (CNN). The research developed a theoretical framework to validate picture-based fingerprint identification as a feasible alternative to traditional touch-based methods. A modified Automated Fingerprint Identification System (AFIS) model served as the study's foundation. To enhance the model's capabilities, data from two databases, IIT India and SOCOFing, were utilized. The evaluation of the CNN architecture focused on mobile device fingerprint recognition. It emphasized key processes such as data pre-processing, model training, and the optimization of the CNN through a Siamese-CNN approach to boost accuracy and efficiency. Python scripts developed for this purpose were converted to Android code using TensorFlow for deployment on Android devices. Performance metrics, including identification accuracy, processing speed, and resource utilization, were analysed to determine the system's feasibility. The results demonstrated that CNN-based fingerprint identification systems hold significant promise for delivering robust and reliable biometric authentication on smartphones, highlighting both their practical applications and limitations. decrease medical as well as financial burden, hence improving the management of cirrhotic patients. These predictors, however, need further work to validate reliability.
13
M., Azhagiri. "Touchless ATM Using Augmented Reality Using TOTP Haar Cascade Algorithm." International Journal of Soft Computing and Engineering (IJSCE) 15, no. 1 (2025): 5–9. https://doi.org/10.35940/ijsce.F3506.15010325.
Full textAbstract:
<strong>Аbstrасt: </strong>Touchless ATMs, a new technology, offer a contactfree, hygienic, and convenient financial transaction experience. This innovative solution uses Augmented Reality (AR), Timebased One-Time Passwords (TOTP), and the HAAR Cascade Algorithm to create an interactive virtual interface, reducing physical contact and enhancing transaction security. The system uses a dual-layered authentication mechanism, utilizing facial recognition and time-based, one-time passwords (TOTP) to validate user identities and generate dynamic, session-specific codes. Financial institutions can deploy this system to upgrade their ATM networks, catering to diverse user demographics. Challenges include developing robust gesture recognition models, ensuring low latency in AR interactions, and integrating these advanced technologies into existing ATM infrastructures. However, advances in hardware and software, coupled with the decreasing cost of AR and machine learning technologies, make this solution viable and scalable.
14
Prof. Amol T. Take, Sudipta Mondal, Nilesh Musale, Sanjana Sawant, and Manasi Singh. "Smart HygineMate Hub: A Smart Vending Machine." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 354–64. http://dx.doi.org/10.32628/cseit24103117.
Full textAbstract:
Activists and groups concerned with women’s health and hygiene as well as news articles, social media, and discussions have brought attention to the issue of sanitary pads selling machines. The need of convenient access to sanitary pads through vending machines particularly in public locations like schools, colleges, airports, hotels and public restrooms, was made more widely known through the efforts of Women’s Rights Organization, Non-Governmental Organizations (NGOs) and Government Initiatives. As a part of larger discussion surrounding menstrual hygiene and women’s empowerment this topic attracted attention. By automating inventory management, providing touchless interactions, various payment modes and enhancing security, the model provides a streamlined user experience. This technology also yields a valuable data insight, reduced maintenance efforts and facilitates personalized user profiles. Ultimately it contributes in improving menstrual hygiene and accessibility. The lack of scanner which makes it difficult for people in need of sanitary pads in emergencies, the difficulty of promptly monitoring and replenishing the machines, sanitary pads supply are the two of the main issues with the existing sanitary pad vending machines. Additionally consistent resources and work are needed to maintain and keep sanitary pad vending machines operational. Further issue still exists of affordability of the vending machines particularly in the rural areas due to limited resources. These issues mainly attracted to work and research on making a vending machine which will be cost effective, simplified and user-friendly machines with the use of edge cutting technologies.
15
Prakash Veigas, John, and Sharmila Kumari M. "Deep learning approach for Touchless Palmprint Recognition based on Alexnet and Fuzzy Support Vector Machine." International journal of electrical and computer engineering systems 13, no. 7 (2022): 551–59. http://dx.doi.org/10.32985/ijeces.13.7.7.
Full textAbstract:
Due to stable and discriminative features, palmprint-based biometrics has been gaining popularity in recent years. Most of the traditional palmprint recognition systems are designed with a group of hand-crafted features that ignores some additional features. For tackling the problem described above, a Convolution Neural Network (CNN) model inspired by Alex-net that learns the features from the ROI images and classifies using a fuzzy support vector machine is proposed. The output of the CNN is fed as input to the fuzzy Support vector machine. The CNN's receptive field aids in extracting the most discriminative features from the palmprint images, and Fuzzy SVM results in a robust classification. The experiments are conducted on popular contactless datasets such as IITD, POLYU2, Tongji, and CASIA databases. Results demonstrate our approach outperformers several state-of-art techniques for palmprint recognition. Using this approach, we obtain 99.98% testing accuracy for the Tongji dataset and 99.76 % for the POLYU-II datasets.
16
Sharma, Aditya. "Smart Face Attendance System using Facial Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem36137.
Full textAbstract:
The SmartFace Attendance system using facial recognition represents a modern solution to automate attendance tracking processes in educational institutions and organizations. Leveraging advanced facial recognition technology, it offers accurate and efficient attendance management while addressing privacy concerns and ensuring user acceptance. The system utilizes state-of-the-art algorithms such as Convolutional Neural Networks (CNN), Histogram of Oriented Gradients (HOG), and Support Vector Machines (SVM) to detect and recognize faces in real-time captured images. By providing touchless operation and seamless integration with existing infrastructure, SmartFace Attendance system offers convenience and scalability for users across diverse environments. With robust security measures, adherence to ethical considerations, and compliance with legal regulations, it is poised to revolutionize attendance tracking practices, enhancing operational efficiency and improving user experiences in educational and organizational settings. Keywords—Online Attendance System, Energy Efficiency, Facial Recognition, Support Vector Machine(SVM), Decision Tree, Histogram of Oriented Gradients(HOG), Convolutional Neural Network(CNN), Database Management, Model Train- ing.
17
Bockhacker, Markus, Hannah Syrek, Max Elstermann von Elster, Sebastian Schmitt, and Henning Roehl. "Evaluating Usability of a Touchless Image Viewer in the Operating Room." Applied Clinical Informatics 11, no. 01 (2020): 088–94. http://dx.doi.org/10.1055/s-0039-1701003.
Full textAbstract:
Abstract Background Availability of patient-specific image data, gathered from preoperatively conducted studies, like computed tomography scans and magnetic resonance imaging studies, during a surgical procedure is a key factor for surgical success and patient safety. Several alternative input methods, including recognition of hand gestures, have been proposed for surgeons to interact with medical image viewers during an operation. Previous studies pointed out the need for usability evaluation of these systems. Objectives We describe the accuracy and usability of a novel software system, which integrates gesture recognition via machine learning into an established image viewer. Methods This pilot study is a prospective, observational trial, which asked surgeons to interact with software to perform two standardized tasks in a sterile environment, modeled closely to a real-life situation in an operating room. To assess usability, the validated “System Usability Scale” (SUS) was used. On a technical level, we also evaluated the accuracy of the underlying neural network. Results The neural network reached 98.94% accuracy while predicting the gestures during validation. Eight surgeons with an average of 6.5 years of experience participated in the usability study. The system was rated on average with 80.25 points on the SUS. Conclusion The system showed good overall usability; however, additional areas of potential improvement were identified and further usability studies are needed. Because the system uses standard PC hardware, it made for easy integration into the operating room.
18
Masriani, Masriani, Sahrul Alam, and Arham Arham. "Rancang Bangun Sistem Automatic Touchless Mask Machine Upaya Pengendalian Penggunaan Masker di Masa Pandemi Covid-19." JURNAL UNITEK 15, no. 1 (2022): 9–19. http://dx.doi.org/10.52072/unitek.v15i1.314.
Full textAbstract:
Penelitian ini bertujuan untuk menghasilkan produk dalam hal ini berupa Rancang Bangun Sistem Automatic Touchless Mask Machine Dalam Upaya Pengendalian Penggunaan Masker Di Masa Pandemi Covid-19 untuk memudahkan masyarakat mengambil masker ditempat-tempat umum ketika lupa membawa masker ataupun tidak mempunyai masker. Penelitian ini menggunakan jenis penelitian D&D (Desigh & Development) dengan model pengembangan waterfall. Teknik pengumpulan data yang digunakan dalam penelitian ini yaitu angket, observasi, dan wawancara. Hasil penelitian ini telah melalui uji coba serta hasil analisis data yang diperoleh sesuai denga apa yang diharapkan oleh tim peneliti. Hasil pengujian fungsionality dalam hal ini dilakukan oleh 2 orang validator ahli atau expert dibidang teknologi pada produk berfungsi sesuai dengan rancangan awal, dan untuk hasil pengujian pengguna terdapat 19 responden yang memberikan nilai dengan persentase 76%-100% (Sangat Layak). Sedangkan 1 responden lainnya memberikan nilai dengan persentase 51%-75% (Baik).
19
Manfaluthy, Mauludi, Devan Junesco, and Muhammad Assadullah. "Desain Mesin Penyedia Obat di Masa Pandemi Covid-19 untuk Kegiatan Pengabdian Masyarakat di Kelurahan Jatimelati Pondok Melati Bekasi." Jurnal Abdimas ADPI Sains dan Teknologi 3, no. 4 (2022): 12–16. http://dx.doi.org/10.47841/saintek.v3i4.257.
Full textAbstract:
The surge in positive cases of Covid-19 in Indonesia has increased the demand for medicines and vitamins. As a result, many residents visited the village health center, pharmacies, and drugstores to buy medicine or vitamins to boost their body's immunity during a pandemic. health protocols that limit room capacity result in queues of buyers snaking which can lead to the potential for new infections. The World Health Organization (WHO) itself urges people to implement contactless payments, by using e-money or using electronic payments and avoiding touching touchless devices. This research is a continuation of the activity plan for Community Service at the Jatimelati village health center Pondok Melati Bekasi. Machines providing medicines that accommodate contactless and face-to-face contact with officers are deemed necessary to meet the needs of medicines and vitamins as a complement to machines providing medicines and vitamins so that people can get them more easily during and after the pandemic. In this study, this drug supply machine is equipped with a payment system using a balanced RFID card such as e-money and is also integrated with the website to facilitate monitoring of drug stock availability.
20
Prasad, Shubham, Shivam Singh, Preeti Singh, Shubhangi Srivastava, and Dr Ajay Sahu. "IoT Based Gesture Control Light System Using Python." International Journal of Innovative Research in Advanced Engineering 11, no. 11 (2024): 847–53. https://doi.org/10.26562/ijirae.2024.v1111.09.
Full textAbstract:
The Gesture Control Light System is an innovative, user-friendly interface designed to enhance the interaction between humans and electronic devices. By utilizing hand gestures as input commands, this system enables users to control lighting appliances without physical contact. It integrates gesture recognition technology, primarily using infrared sensors or cameras, to interpret specific hand movements like swipes or waves, translating them into actions such as turning lights on/off, adjusting brightness, or changing colour modes. This touchless approach offers convenience and hygiene benefits, especially in settings where hands-free control is advantageous, such as in smart homes, medical facilities, or during cooking tasks. The system's intuitive design also contributes to energy efficiency, enabling automation and personalized lighting preferences. With its practical applications and potential to improve user experience, the Gesture Control Light System represents a step forward in the evolution of smart technology and human-machine interaction.
21
Malik, Anuj. "AI Virtual Mouse." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5874–80. https://doi.org/10.22214/ijraset.2025.71568.
Full textAbstract:
The AI Virtual Mouse is a revolutionary touchless replacement to standard computer mice that prioritizes hygiene and simplicity in an increasingly health-conscious world. Even while wireless mice do away with cable-related problems, they still require physical contact, which might spread germs, especially in offices where workstations are shared. By using an external or built-in camera on a computer to record and analyze hand and finger movements in real time, this state-of-the-art technology leverages computer vision and machine learning to do away with the need for touch. The AI Virtual Mouse accurately recognizes hand features using deep neural networks, allowing it to perform basic mouse activities including clicking, scrolling, and pointer navigation without requiring human interaction. This idea, which uses OpenCV in Python, enhances hygiene by significantly reducing the risk of contamination linked to high-touch surfaces while simultaneously improving user experience by doing away with the requirement for physical peripherals.
22
Bhandari R, Aishwarya, Monika B S, Ashika R, Rakshitha D, and Mr Krishna Swaroop. "Hand Gesture Recognition System Using Thermal Images: A Systematic Literature Review." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42790.
Full textAbstract:
Leading the way in human-computer interaction (HCI) advancements are hand gesture recognition (HGR) systems, which provide touchless, intuitive control for a wide range of applications, including healthcare, smart devices, assistive technologies, and virtual environments. In order to demonstrate the development of HGR methodologies, such as vision-based, skeleton-based, and hybrid approaches, this paper summarizes the results of recent studies. The field is dominated by vision-based systems, which use depth sensors and RGB cameras to recognize both static and dynamic gestures. Particularly for continuous gesture recognition, skeleton-based methods such as the TD-Net architecture offer computationally effective and lightweight solutions. While multimodal systems that combine RGB, thermal, and depth data improve robustness, thermal imaging expands the applicability of HGR to low-light conditions. Despite advancements, problems still exist, such as signer-independent performance, environmental adaptability, and gesture spotting in continuous streams. Directions for the future. Keywords— Machine learning, Deep learning, Hand Gesture, image processing, Convolutional neural networks, Thermal images.
23
Kurz, Marc, Robert Gstoettner, and Erik Sonnleitner. "Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition." Applied Sciences 11, no. 5 (2021): 2015. http://dx.doi.org/10.3390/app11052015.
Full textAbstract:
Since electronic components are constantly getting smaller and smaller, sensors and logic boards can be fitted into smaller enclosures. This miniaturization lead to the development of smart rings containing motion sensors. These sensors of smart rings can be used to recognize hand/finger gestures enabling natural interaction. Unlike vision-based systems, wearable systems do not require a special infrastructure to operate in. Smart rings are highly mobile and are able to communicate wirelessly with various devices. They could potentially be used as a touchless user interface for countless applications, possibly leading to new developments in many areas of computer science and human–computer interaction. Specifically, the accelerometer and gyroscope sensors of a custom-built smart ring and of a smartwatch are used to train multiple machine learning models. The accuracy of the models is compared to evaluate whether smart rings or smartwatches are better suited for gesture recognition tasks. All the real-time data processing to predict 12 different gesture classes is done on a smartphone, which communicates wirelessly with the smart ring and the smartwatch. The system achieves accuracy scores of up to 98.8%, utilizing different machine learning models. Each machine learning model is trained with multiple different feature vectors in order to find optimal features for the gesture recognition task. A minimum accuracy threshold of 92% was derived from related research, to prove that the proposed system is able to compete with state-of-the-art solutions.
24
Makushko, Pavlo, Eduardo Sergio Oliveros Mata, Gilbert Santiago Cañón Bermúdez, et al. "Flexible Magnetoreceptors: Flexible Magnetoreceptor with Tunable Intrinsic Logic for On‐Skin Touchless Human‐Machine Interfaces (Adv. Funct. Mater. 25/2021)." Advanced Functional Materials 31, no. 25 (2021): 2170184. http://dx.doi.org/10.1002/adfm.202170184.
Full text
25
V, Abhiyashvi, Joshini Priya .S, Saravanan Elumalai, and Ananthan .T.V. "Gesture-Controlled Contactless Switch for Smart Home." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43444.
Full textAbstract:
The innovation of smart home automation has developed contactless control systems that maximize convenience, accessibility, and sanitation. This article introduces a contactless switch with gesture control meant to control household appliances with gestures from the hands. The system applies infrared, ultrasonic, or camera-based sensors to read and analyze the gestures of users in real time without requiring direct contact. Machine learning algorithms are utilized to enhance the accuracy of gesture recognition, and wireless communication protocols like Wi-Fi and Bluetooth allow for smooth integration with smart home systems. The system presented is an energy-efficient and long-lasting substitute for conventional switches, minimizing wear and tear while providing improved user experience. Experimental outcomes demonstrate the efficiency of the system in identifying gestures with high precision and operating appliances effectively, and it has the potential for use on a large scale in smart homes. The research contributes to the growing corpus of human-computer interaction by providing an effective and real-world solution for touchless home automation. Key words: Gesture recognition, Contactless switch, Smart home automation, Human-computer interaction Wireless control
26
Vanmore, Sakshi Chandrakant. "Gesture Controlled Robotic Arm Using OpenCV." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem04698.
Full textAbstract:
The evolution of human-computer interaction has led to the development of intuitive and contactless interfaces, among which gesture-based control stands out due to its natural and user-friendly approach. This project focuses on designing and implementing a gesture- controlled robotic arm that interprets hand gestures in real time and converts them into physical movements of a robotic arm. The system aims to provide a seamless interface between human intent and robotic action, particularly beneficial in areas where touchless control is essential, such as healthcare, hazardous environments, and assistive technology. processed using OpenCV in Python to calculate hand center coordinates and finger positions. The core of the system leverages MediaPipe, a robust machine learning framework developed by Google, to detect and track 21 hand landmarks from a standard webcam feed. These landmarks are By analyzing the number of fingers raised and the location of the palm on the screen. Key Words: Python, open CV, mediaPipe, pyfirmata, Arduino Uno microcontroller, servo motors, and a custom-built robotic arm
27
Asmoro, Jeffri Dian, Achmad Teguh Wibowo, and Mujib Ridwan. "VIRTUAL MOUSE WITH HAND GESTURE RECOGNITION BASED ON HAND LANDMARK MODEL FOR POINTING DEVICE." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 9, no. 2 (2023): 261–68. http://dx.doi.org/10.33330/jurteksi.v9i2.2073.
Full textAbstract:
Abstract: Technology is growing rapidly and has become one of the human needs that must be owned to solve the problems being faced. The development of touchless input devices or hand gesture recognition using a camera is a form of machine learning. Gestures can define as physical movements of the hands, arms, or body as expressive messages, besides that this hand gesture system can explain the contents of commands that have meaning. In this research, a virtual mouse system will be developed using hand gesture recognition based on the hand landmark model for pointing devices. The resulting application can be run on a desktop device using a webcam. The results of the tests carried out to analyze the implementation of the hand landmark model into the system show that the average system accuracy reaches 96% and the speed reaches 0.05 seconds. Keywords: hand gesture recognition, hand landmark models, machine learning, virtual mouse Abstract: Teknologi semakin pesat dan sudah menjadi salah satu kebutuhan manusia yang harus dimiliki untuk menyelesaikan permasalahan yang sedang dihadapi. Perkembangan piranti masukan tanpa sentuhan atau hand gesture recognition menggunakan kamera adalah salah satu bentuk dari machine learning. Gestur mampu mendefinisikan sebagai gerakan fisik dari tangan, lengan, maupun badan sebagai pesan yang ekspresif, selain itu sistem gestur tangan ini mampu menjelaskan isi perintah yang memiliki arti. Dalam penelitian ini akan dikembangkan sebuah sistem virtual mouse menggunakan hand gesture recognition berbasis hand landmark model untuk pointing device. Aplikasi yang dihasilkan dapat dijalankan pada perangkat desktop dengan menggunakan webcam. Hasil dari pengujian yang dilakukan untuk menganalisa penerapan hand landmark model kedalam sistem menunjukkan rata-rata akurasi sistem mencapai 96% dan kecepatan mencapai 0.05 second. Keywords: hand gesture recognition, hand landmark models, machine learning, virtual mouse
28
Pholsook, Thitinan, Sarawut Ramjan, and Warit Wipulanusat. "Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia." Engineering Management in Production and Services 17, no. 2 (2025): 37–62. https://doi.org/10.2478/emj-2025-0011.
Full textAbstract:
Abstract Airports encompass a range of service touchpoints that directly impact passenger satisfaction and, consequently, the likelihood of service recommendation. This study investigates the service quality of Southeast Asian airports by applying five supervised machine learning classification models — decision trees, random forests, support vector machines, neural networks, and gradient boosting machines — on passenger satisfaction data extracted from the Skytrax website. The dataset includes evaluations of various service dimensions, such as staff behaviour, queuing time, and overall experience. This study incorporates cross-validation and hyperparameter tuning to identify the most suitable model for classifying passenger satisfaction. Among the models tested, the random forest classifier achieved the highest accuracy (0.91), demonstrating strong robustness and interpretability. Model performance was assessed using confusion matrices, balanced accuracy, the Matthews correlation coefficient (MCC), and ROC curves. Furthermore, SHAP values were used to identify the most influential service touchpoints, highlighting airport staff performance and queue management as key factors. These findings align with existing literature emphasising the pivotal role of well-trained airport employees and efficient queuing systems in shaping positive passenger experiences. Studies have shown that courteous staff interactions, efficient conflict resolution, and reduced waiting times significantly contribute to customer satisfaction and loyalty. Additionally, the integration of smart technologies such as self-service kiosks, automated security systems, and touchless check-in and baggage solutions enhances operational efficiency and aligns with sustainability initiatives. This study offers a data-driven approach for airport managers to optimise service delivery, increase passenger experiences, and tailor improvements to specific airport environments.
29
Alsheikhy, Ahmed A., Yahia F. Said, and Tawfeeq Shawly. "Continuous Heartbeat Prediction Using a Face Recognition Algorithm." Traitement du Signal 39, no. 5 (2022): 1501–6. http://dx.doi.org/10.18280/ts.390506.
Full textAbstract:
Health providers use the ECG machine to get information about the heart. This information plays a significant role since it tells them about the status of the heart. The ECG machine presents this information in a waveform. During the Covid-19 pandemic, all governments have placed numerous rules and policies to protect people from the virus and from spreading it. One of the rules and policies is to prevent touching surfaces in public places. However, in health care centers, touching surfaces can’t be avoided completely since there is a need to touch them or place some wires on the human body such as placing wires to use the ECG machine. In Saudi Arabia, the government has placed a policy in all its buildings, public places, and the private sector to measure the temperature at the entrance. Due to this situation, the idea has come into mind to have a touchless method to measure the heartbeat rate. In this paper, proposing a feasible and reliable method to estimate a continuous heartbeat rate is presented. It uses a face recognition approach to predict the heart pulse continuously in real-time according to colors intensity measurement. Using a segmentation algorithm is involved since the approach takes its input from a video or an image. Several experiments have been conducted on volunteers to verify the obtained results and measure their relative errors. Consequently, the errors are less than 7% which is quite acceptable. At the end of this article, a comparative assessment is performed between the presented approach and some works from literature. This assessment is conducted based on the methodologies being utilized and applied and Mean Absolute Error (MAE). Furthermore, it shows whether those methods require physical contact or not. The obtained results indicate that the implemented system herein outperforms other state-of-the-art methods.
30
Iqbal, Muhammad, Eleni Mangina, and Abraham Campbell. "Current Current Challenges and Future Research Directions in Augmented Reality for Education." Multimodal Technologies and Interaction 6, no. 9 (2022): 75. http://dx.doi.org/10.3390/mti6090075.
Full textAbstract:
The progression and adoption of innovative learning methodologies signify that a respective part of society is open to new technologies and ideas and thus is advancing. The latest innovation in teaching is the use of Augmented Reality (AR). Applications using this technology have been deployed successfully in STEM (Science, Technology, Engineering, and Mathematics) education for delivering the practical and creative parts of teaching. Since AR technology already has a large volume of published studies about education that reports advantages, limitations, effectiveness, and challenges, classifying these projects will allow for a review of the success in the different educational settings and discover current challenges and future research areas. Due to COVID-19, the landscape of technology-enhanced learning has shifted more toward blended learning, personalized learning spaces and user-centered approach with safety measures. The main findings of this paper include a review of the current literature, investigating the challenges, identifying future research areas, and finally, reporting on the development of two case studies that can highlight the first steps needed to address these research areas. The result of this research ultimately details the research gap required to facilitate real-time touchless hand interaction, kinesthetic learning, and machine learning agents with a remote learning pedagogy.
31
Kotari,, Gopinadh. "Augmented Virtual Mouse System with Enhanced Gesture Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31709.
Full textAbstract:
This paper presents the development of a contactless system designed to serve as an alternative to the traditional physical input device, such as a computer mouse or touchpad, utilized in human-computer interaction. The proposed system aims to offer a more convenient and hygienic way of interacting with computer devices by using hand gestures instead of physical contact with the mouse system or machine interface. The system has potential applications in various fields including healthcare, public interfaces, and gaming. Existing gesture-controlled input systems lack a comprehensive touchless and accurate system capable of performing almost every operation of a physical mouse. The proposed approach involves recognising the hand and its gestures. Hand tracking and dynamic gesture recognition are incorporated to ensure a smooth and hassle-free user experience during interaction. The efficient Mediapipe framework is used to detect and track hand movements from video frames captured through a computing device Camera.Computer vision algorithms are employed for gesture recognition within each frame, along with Python libraries for processing gestures. Sufficient gestures are added into the system, enabling it to execute a wide range of mouse functions with substantial precision metrics. This paper provides details regarding the development and overall performance of the system. Key Words: hands, mouse, contactless, gesture, OpenCV, mediapipe
32
Qi, Wen, Xiaorui Liu, Longbin Zhang, Lunan Wu, Wenchuan Zang, and Hang Su. "Adaptive sensor fusion labeling framework for hand pose recognition in robot teleoperation." Assembly Automation 41, no. 3 (2021): 393–400. http://dx.doi.org/10.1108/aa-11-2020-0178.
Full textAbstract:
Purpose The purpose of this paper is to mainly center on the touchless interaction between humans and robots in the real world. The accuracy of hand pose identification and stable operation in a non-stationary environment is the main challenge, especially in multiple sensors conditions. To guarantee the human-machine interaction system’s performance with a high recognition rate and lower computational time, an adaptive sensor fusion labeling framework should be considered in surgery robot teleoperation. Design/methodology/approach In this paper, a hand pose estimation model is proposed consisting of automatic labeling and classified based on a deep convolutional neural networks (DCNN) structure. Subsequently, an adaptive sensor fusion methodology is proposed for hand pose estimation with two leap motions. The sensor fusion system is implemented to process depth data and electromyography signals capturing from Myo Armband and leap motion, respectively. The developed adaptive methodology can perform stable and continuous hand position estimation even when a single sensor is unable to detect a hand. Findings The proposed adaptive sensor fusion method is verified with various experiments in six degrees of freedom in space. The results showed that the clustering model acquires the highest clustering accuracy (96.31%) than other methods, which can be regarded as real gestures. Moreover, the DCNN classifier gets the highest performance (88.47% accuracy and lowest computational time) than other methods. Originality/value This study can provide theoretical and engineering guidance for hand pose recognition in surgery robot teleoperation and design a new deep learning model for accuracy enhancement.
33
Oliver La Rosa, Jaime E. "Theremin in the Press: Instrument remediation and code-instrument transduction." Organised Sound 23, no. 3 (2018): 256–69. http://dx.doi.org/10.1017/s135577181800016x.
Full textAbstract:
This article shows how the theremin as a new musical medium enacted a double logic throughout its century-old techno-cultural life. On the one hand, in an attempt to be a ‘better’ instrument, the theremin imitated or remediated traditional musical instruments and in this way affirmed the musical values these instruments materialised; simultaneously, by being a new and different medium, with unprecedented flexibility for designing sound and human–machine interaction, it eroded and challenged these same values and gradually enacted change. On the other hand, the theremin inadvertently inaugurated a practice of musical instrument circulation using electronics schematics that allowed for the instrument’s reproduction, starting with the publication of schematics and tutorials in amateur electronics magazines and which can be seen as a predecessor to today’s circulation of open source code. This circulation practice, which I call instrument-code transduction, emerged from and was amplified by the fame the theremin obtained using its touchless interface to imitate or remediate traditional musical instruments, and in turn, this circulation practice has kept the instrument alive throughout the decades. Thus remediation and code-instrument transduction are not just mutually dependent, but are in fact, two interdependent processes of the same media phenomenon. Drawing from early reactions to the theremin documented in the press, from new media theory, and from publications in amateur electronics, this article attempts to use episodes from the history of the theremin to understand the early and profound changes that electric technologies brought to the concept of musical instruments at large.
34
Ionut-Cristian, Severin, and Dobrea Dan-Marius. "Using Inertial Sensors to Determine Head Motion—A Review." Journal of Imaging 7, no. 12 (2021): 265. http://dx.doi.org/10.3390/jimaging7120265.
Full textAbstract:
Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases—IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.
35
Hussain, Tahir, Dostdar Hussain, Israr Hussain, et al. "Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems." Computational and Mathematical Methods in Medicine 2022 (February 12, 2022): 1–17. http://dx.doi.org/10.1155/2022/5137513.
Full textAbstract:
Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.
36
Chiruhin, D. A., and K. V. Ryabinin. "Additive Manufacturing of Personalized Brain-Computer Interface Headsets Reinforced by Scientific Visualization." Scientific Visualization 16, no. 5 (2024): 151–63. https://doi.org/10.26583/sv.16.5.10.
Full textAbstract:
Recently, a large attention has been attracted to the brain-computer human-machine interfaces (BCI) based on electroencephalography (EEG). This emerging technology allows touchless control over digital systems, in which the commands are based on human brain activity. In the ideal case, it means controlling the systems virtually by thoughts, but in reality, also simpler approaches are highly demanded like reacting to concentration, relaxation, or specific emotions. Modern BCIs are based on detecting so-called brain waves, the electromagnetic field oscillations induced by brain neurons. These waves are captured by electrodes either intruded into the brain or placed on top of the head. Obviously, placing electrodes on the head is more demanded for non-medical applications of BCI because it is absolutely harmless for the person. To achieve this, special headsets are needed which can be put on the head like a helmet and ensure the correct positions for the electrodes mounted on them. In this regard, wearing comfort and anatomical accuracy of headsets play an important role in ensuring both ergonomics and precision of BCI. This paper focuses on automation of the personalized EEG headset manufacturing for BCI. The technological chain is proposed and corresponding software tools are developed to foster the complete cycle of BCI headset production for a particular person. The production steps include 3D scanning of the head, interactive editing of the electrodes’ location system, and automatic generation of a collapsible head cap model with sockets for EEG electrodes optimized for 3D printing. The performance of the pipeline has been validated in practice. The accuracy of electrodes’ placement has been evaluated by comparison with the head cap from professional medical equipment and is established as sufficient for BCI. The headset model editing and customizing tools are powered with scientific visualization and cognitive graphics techniques to be friendly for a wide range of users including those with no dedicated IT skills.
37
Justina, J. P., and Sangeetha Senthilkumar. "An Efficient Vision-Based Hand Beckon Perception for Physically Debilitated People using MCMC and HMM." Asian Journal of Electrical Sciences 4, no. 1 (2015): 34–44. http://dx.doi.org/10.51983/ajes-2015.4.1.1931.
Full textAbstract:
Recognition of hand gestures has a significant impact on human society. It is a natural and intuitive way to provide the interaction between human and the computer. It provides touchless interaction and easy way to interact without any external devices. With the ever increasing role of computerized machines in society, Human Computer Interaction (HCI) system has become an increasingly important part of our daily lives. HCI determines the effective utilization of the available information flow of the computing, communication, and display technologies. Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. Hidden Markov models (HMMs) and related models have become standard in statistics with applications in diverse areas. Markov chain Monte Carlo (MCMC) is great stuff. MCMC revitalized Bayesian inference and frequents inference about complex dependence. A high performance Artificial Neural Network (ANN) classifier is employed to improve the classification and accuracy.
38
Placko, D. "Thickness touchless measurements using eddy current sensors Electric machines and power systems, Vol. 17, No. 2, pp. 125–137 (1989)." NDT & E International 23, no. 6 (1990): 357. http://dx.doi.org/10.1016/0963-8695(90)90167-h.
Full text
39
Nagtilak, Prof S. A. "Survey on Gesture-Based Virtual Keyboard and Mouse." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28414.
Full textAbstract:
Envisioning a future where the conventional mouse and keyboard inputs are supplanted by a touchless system that harnesses the power of eye tracking and hand gestures. This visionary transformation in human-computer interaction is not without its challenges, and at the heart of this pursuit lies the goal of achieving unmatched precision and user-friendliness. This innovative system is underpinned by a fusion of technology and creativity. Hand gestures, a symbol of human expressiveness, are seamlessly integrated with Haar Cascade, offering keyboard operation and mouse control. This elegant synergy empowers users to navigate digital landscapes intuitively, minimizing the barriers between humans and machines. However, this journey is marked by the quest for absolute precision and calibration. Insights from past research underscore the significance of precision in interpreting and responding to user gestures, be it the intricate movements of the hand or the subtleties of gaze tracking. Bridging this gap, it aim to make every interaction fluid and error-free, while maintaining a commitment to user-friendliness. Given methodology unfolds as an intricate dance of technology and innovation. It involves the real-time processing of hand gestures and eye tracking data, intricate data fusion in the Gesture Recognition System, and the seamless updating of the user interface. The potential applications of this technology are as diverse as they are impactful. From enhancing accessibility for individuals with disabilities to providing hygienic control of medical equipment in healthcare settings, from delivering immersive gaming experiences to facilitating interactive learning environments in education, the reach of this system extends across various domains. This research is a testament to the belief that technology should be an enabler, not an obstacle. It is a journey that involves overcoming challenges in pursuit of a user-centric experience. Key Words: AR, CNN, CPU, IMU, VR
40
Wang, Jinrong, Weibin Lin, Zhuo Chen, Valeriia O. Nikolaeva, Lukman O. Alimi, and Niveen M. Khashab. "Smart touchless human–machine interaction based on crystalline porous cages." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-024-46071-8.
Full textAbstract:
AbstractThe rise of touchless technology, driven by the recent pandemic, has transformed human-machine interaction (HMI). Projections indicate a substantial growth in the touchless technology market, nearly tripling from $13.6 billion in 2021 to an estimated $37.6 billion by 2026. In response to the pandemic-driven shift towards touchless technology, here we show an organic cage-based humidity sensor with remarkable humidity responsiveness, forming the basis for advanced touchless platforms in potential future HMI systems. This cage sensor boasts an ultrafast response/recovery time (1 s/3 s) and exceptional stability (over 800 cycles) across relative humidity (RH) changes from 11% to 95%. The crystal structure’s 3D pore network and luxuriant water-absorbing functional groups both inside and outside of the cage contribute synergistically to superior humidity sensing. Demonstrating versatility, we showcase this cage in smart touchless control screens and touchless password managers, presenting cost-effective and easily processable applications of molecularly porous materials in touchless HMI.
41
Gao, Yangchen, Jie Yang, Yijie Luo, et al. "Bioinspired Self‐Assembled Gradient‐Structured Dual‐Modal Sensor with Extended Range and Durability." Advanced Functional Materials, July 9, 2025. https://doi.org/10.1002/adfm.202507079.
Full textAbstract:
AbstractThe growing demand for advanced sensors in intelligent robotics, augmented reality (AR/VR), embodied intelligence, and human‐machine interaction (HMI) has driven significant interest in dual‐modal sensors capable of both tactile and touchless sensing, particularly for enhancing accuracy and adaptability in dynamic and complex environments. However, current designs often face challenges related to interfacial mismatches between layers, resulting in delamination and compromised durability under mechanical stress. Here, inspired by the sensory systems of knifefish, a novel dual‐modal sensor is presented that employs gravity‐driven self‐assembly to integrate 2D transition metal borides (MBene) nanosheets and reduced graphene oxide (rGO) within a porous polyurethane foam (PUF) matrix. This gradient‐structured composite improves stress dispersion, sensitivity, and stability by leveraging the Maxwell‐Wagner polarization, leading to enhanced dielectric properties and extended touchless sensing capabilities. The sensor achieves a wide pressure detection range (16 Pa‐11.3 MPa), exceptional durability (>200 000 cycles), and a touchless sensing range of up to 25 cm. Demonstrated applications, including robotic fruit sorting, remote handwriting, and touchless piano performance, highlight the sensor's potential to drive the development of next‐generation intelligent electronic devices.
42
Liu, Weijie, Feihe Xiang, Deqing Mei, and Yancheng Wang. "A Flexible Dual‐Mode Capacitive Sensor for Highly Sensitive Touchless and Tactile Sensing in Human‐Machine Interactions." Advanced Materials Technologies, November 29, 2023. http://dx.doi.org/10.1002/admt.202301685.
Full textAbstract:
AbstractHuman‐machine interaction(HMI) is extensively employed in various applications such as robotic control and augmented reality/virtual reality. However, HMIs are mainly based on single‐interaction mode, requiring bulky equipment to be worn or sustaining physical contact with the interfaces, resulting in limited interaction efficiency and intelligence. Here, it proposes a novel flexible dual‐mode capacitive sensor using 12 sensing units for touchless and tactile sensing during HMIs, the sensor has a labyrinth‐patterned electrode to improve the performance of proximity sensing and a truncated pyramid‐shaped porous hierarchical dielectric structure to improve the pressure sensing performance. The fabricated dual‐mode sensor is characterized by a high proximity detection range of up to 110 mm, the pressure detection range is from 0 to 200 kPa with a sensitivity of 0.464% kPa−1. Further, the dual‐mode sensor exhibits excellent discrimination between proximity and pressure signals, along with remarkable stability and repeatability. Then, a touchless‐tactile HMI platform is developed for real‐time control of robotics through accurate perception of touchless hand gestures and contact‐pressing interactions. This platform demonstrates the superior dual‐mode sensing performance of the sensor and validates its potential in future intelligent HMI scenarios.
43
Iqbal, Muhammad Zahid, and Abraham G. Campbell. "Potential security and privacy issues in zero UI touchless technology." International Cybersecurity Law Review, April 19, 2022. http://dx.doi.org/10.1365/s43439-022-00052-z.
Full textAbstract:
AbstractTouchless technology often called Zero User Interface (UI) has begun to permeate every aspect of our lives as its use became necessary for hygiene measures in public places. The evolution of touchless technology replacing touchscreen interaction started as a luxury concept to give a fancier look to digital interactions, but now it has gained real value as a health-oriented interaction method. Switching to a touchless interface reduces common touchpoints, which help to safeguard against the spread of pathogens. Although the evolution of touchless technology is not new, its use massively increased due to its inherent hygienic nature during the COVID-19 pandemic. However, this investment in a new form of digital interaction has several privacy and security issues that need attention, in order to allow for safe human–machine interaction to cope with security breaches and cyber-attacks to protect our credentials. This paper outlines the potential security and privacy issues concerning Zero UI adoption in various technologies that need to be considered if one wishes to adopt responsible technology practices with this technology.
44
Zhang, Chunlei, Ming Wang, Wenbo Li, et al. "Shadow‐Thermoelectric System for Enhancing Solar Energy Harvesting and Touchless Human‐Machine Interface." Advanced Functional Materials, April 24, 2025. https://doi.org/10.1002/adfm.202504693.
Full textAbstract:
AbstractThermoelectric generators (TEGs) are one of the most promising means of harvesting energy from the sun. However, the TEGs require a significant temperature difference to generate electricity, making it challenging to achieve high power output from solar radiation alone. Here, shadow‐effect generators (SEGs) are connected with TEGs to form a shadow‐thermoelectric system (STS), which exploits the natural shadow produced by the TEG to enhance the electrical power output by solar energy conversion. With the shadow enhancement, the open‐circuit voltage of the STS is significantly increased by a factor of 115 compared to that of the single TEG. Furthermore, the STSs array is successfully combined with a photoelectrochemical cell to convert the intermittent solar energy into storable hydrogen energy. In addition, the STS can be applied as a touchless self‐powered sensor which provides a useful control panel for human‐machine interface (HMI). After combining the STS with signal processing circuits, the STS can control a virtual vehicle on computer smartly. In conclusion, STS is a promising design for energy harvesting and touchless self‐powered sensing. This research provides valuable insights for the development of efficient solar energy conversion and new possibilities for HMI.
45
Swathi, B., Akshay S. Prathap, Aiswarya V. Kumar, Ranjitha R, and Raviteja Kaki. "Implementation of Voice based Touchless Lift System." International Journal of Scientific Research in Science and Technology, July 10, 2021, 241–46. http://dx.doi.org/10.32628/cseit217459.
Full textAbstract:
In this rapid world of technology where voice begins its era of domination to replace the touch screens from smart phones to huge computer systems, bringing voice in day to day affairs becomes significant. An elevator or lift is a transport vehicle that moves people or goods from on floor to another floor in a building. Typically push buttons were used to send requests to the elevators. In recent times touch buttons are coming to use. But now voice recognition can replace the push/touch technology. Elevators being one such system used in daily life serves this purpose of making future generations hands free which also becomes a boon for the disabled as well as helps during the pandemic situation to avoid physical contact. The main objective of this project is to propose and assemble a voice operated lift/elevator control system. The proposed system acts as human-machine communication system. This research combines electronic control technology with speech recognition technology. The input to the system is human speech. Speech recognition is the method of recognizing the vocal words to take the essential actions accordingly. This device is very helpful for paralysis, short height people and physically challenged persons.
46
Rajeshkumar, C., and K. Ruba Soundar. "TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm." Technology and Health Care, June 24, 2024, 1–22. http://dx.doi.org/10.3233/thc-240149.
Full textAbstract:
BACKGROUND: Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnormalities. OBJECTIVE: Existing methods for lung abnormality detection often rely on invasive and time-consuming procedures limiting their effectiveness in real-time diagnosis. This work introduces a novel Touchless Lung Abnormality (TO-LAB) detection model utilizing universal software radio peripherals (USRP) and machine learning algorithms. METHODS: The TO-LAB model integrates a blood pressure meter and an RGB-D depth-sensing camera to gather individual data without physical contact. Heart rate (HR) is analyzed through image conversion to IPPG signals, while blood pressure (BP) is obtained via analog conversion from the blood pressure meter. This touchless imaging setup facilitates the extraction of essential signal features crucial for respiratory pattern analysis. Advanced computer vision algorithms like Mel-frequency cepstral coefficients (MFCC) and Principal Component Analysis (PCA) process the acquired data to focus on breathing abnormalities. These features are then combined and inputted into a machine learning-based Multi-class SVM for breathing activity analysis. The Multi-class SVM categorizes breathing abnormalities as normal, shallow, or elevated based on the fused features. The efficiency of this TO-LAB model is evaluated with the simulated and real-time data. RESULTS: According to the findings, the proposed TO-LAB model attains the maximum accuracy of 96.15% for real time data; however, the accuracy increases to 99.54% for simulated data for the efficient classification of breathing abnormalities. CONCLUSION: From this analysis, our model attains better results in simulated data but it declines the accuracy while processing with real-time data. Moreover, this work has a significant medical impact since it presents a solution to the problem of gathering enough data during the epidemic to create a realistic model with a large dataset.
47
Lugoda, Pasindu, Eduardo Sergio Oliveros-Mata, Kalana Marasinghe, et al. "Submersible touchless interactivity in conformable textiles enabled by highly selective overbraided magnetoresistive sensors." Communications Engineering 4, no. 1 (2025). https://doi.org/10.1038/s44172-025-00373-x.
Full textAbstract:
Abstract Miniature electronics positioned within textile braids leverages the persistent flexibility and comfort of textiles constructed from electronics with 1D form factors. Here, we developed touchless interactivity within textiles using 1D overbraided magnetic field sensors. Our integration strategy minimally impacts the performance of flexible giant magnetoresistive sensors, yielding machine-washable sensors that maintain conformability when integrated in traditional fabrics. These overbraided magnetoresistive sensors exhibit a detectivity down to 380 nT and a nearly isotropic magnetoresistance amplitude response, facilitating intuitive touchless interaction. The interactivity is possible even in humid environments, including underwater, opening reliable activation in day-to-day and specialized applications. To showcase capabilities of overbraided magnetoresistive sensors, we demonstrate a functional armband for navigation control in virtual reality environments and a self-monitoring safety helmet strap. This approach bridges the integration gap between on-skin and rigid magnetic interfaces, paving the way for highly reliable, comfortable, interactive textiles across entertainment, safety, and sportswear.
48
Zhou, Hao, Wei Huang, Zhuo Xiao, et al. "Deep‐Learning‐Assisted Noncontact Gesture‐Recognition System for Touchless Human‐Machine Interfaces." Advanced Functional Materials, September 30, 2022, 2208271. http://dx.doi.org/10.1002/adfm.202208271.
Full text
49
An, Shun, Hanrui Zhu, Chunzhi Guo, et al. "Noncontact human-machine interaction based on hand-responsive infrared structural color." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-022-29197-5.
Full textAbstract:
AbstractNoncontact human-machine interaction provides a hygienic and intelligent approach for the communication between human and robots. Current noncontact human-machine interactions are generally limited by the interaction distance or conditions, such as in the dark. Here we explore the utilization of hand as an infrared light source for noncontact human-machine interaction. Metallic gratings are used as the human-machine interface to respond to infrared radiation from hand and the generated signals are visualized as different infrared structural colors. We demonstrate the applications of the infrared structural color-based human-machine interaction for user-interactive touchless display and real-time control of a robot vehicle. The interaction is flexible to the hand-interface distance ranging from a few centimeters to tens of centimeters and can be used in low lighting condition or in the dark. The findings in this work provide an alternative and complementary approach to traditional noncontact human-machine interactions, which may further broaden the potential applications of human-machine interaction.
50
Shivshankar M Navadagi, Arati V Patil, Rohit S Jadhav, Muttu Rugi, Ashok M Hulagabali, and Rajendra Galagali. "Design And Fabrication of Shoe Dusting Machine." international journal of engineering technology and management sciences, July 28, 2022, 46–52. http://dx.doi.org/10.46647/ijetms.2022.v06i04.009.
Full textAbstract:
Our project aims at safety, simplicity and comfort for human use. This project deals with both, the clipper of our effort and incorporating shoe sole dust cleaning facility with this machine. This project aims on automation of shoe sole dust cleaning without any human intimacy. This project presents a basic as well as very professional treatment of the subject in a very comprehensive, based on learning effort and understanding cap ability and needs of today as per their requirements and levels. This automatic shoe sole dust cleaning machine significantly better than the old methods and process of cleaning the shoe sole. Then the shoes dusting machine is designed by considering all the various variables with respect to customer need in terms of transferable and economically available to them, thus providing dust and dirt free environment. This shoes dusting machine provides touchless usage which reduces the time required for cleaning the shoe dust and is more effective.