Towards a Virtual Keyboard Scheme Based on Wearing One Motion Sensor Ring on Each Hand

Lian, Chao; Ren, Xianshou; Zhao, Yuliang; Zhang, Xueliang; Chen, Ruoyu; Wang, Shuyu; Sha, Xiaopeng; Li, Wen J.

doi:10.1109/jsen.2020.3023964

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…The unit of acceleration integral value is per minute. The sum of acceleration integral value is defined as counts/min, which is the average value over a period of time [18]. In signal processing, the second-order cutoff frequency 17 Hz butterworth low-pass filter is used to filter the original signal.…”

Section: Physical Fitness Measurement Methods In Sports Dancementioning

confidence: 99%

Application of Heart Rate Combined with Acceleration Motion Sensor in Sports Dance Teaching

Liu

et al. 2022

Journal of Sensors

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Because of the large amount of exercise, sports dance has the characteristics of good viewing and strong movement, and has gradually entered the campus in recent years. However, sports dance is not only difficult, but also has a wide variety of categories except modern dance and Latin dance. In addition to these two categories, there are 10 kinds of dances in total. Therefore, in teaching, schools have no choice, and students cannot understand which dance styles are most effective for students’ physical exercise. In order to solve the problem that there are many kinds of sports dance and the effect of students’ exercise is unclear, this paper studied sports dance teaching based on heart rate combined with acceleration motion sensor, and obtained the energy consumption in sports dance teaching by using this measurement method. After comparing the effects, four kinds of dance activities, namely quickstep, waltz, rumba, and samba, which are more conducive to physical exercise, were screened out for students in the complex sports dance. In addition, in order to confirm the experimental results of the article, after the teacher improved the physical dance teaching according to the content of the article, it was found that before the experiment, the average scores of the two groups of subjects were 17.5 points and 16.75 points. After the experiment, the scores of the two groups increased by 3.5 points and 2.8 points, respectively. Through the experimental results and data, it can be shown that the research in this paper has played a good role in the improvement of sports dance teaching.

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Section: Physical Fitness Measurement Methods In Sports Dancementioning

confidence: 99%

Application of Heart Rate Combined with Acceleration Motion Sensor in Sports Dance Teaching

Liu

et al. 2022

Journal of Sensors

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“…In this paper, C. Lian et al [4] presented an improved virtual keyboard design using smart rings worn on each hand. The rings have sensors that detect motion during typing.…”

Section: Related Workmentioning

confidence: 99%

Survey on Gesture-Based Virtual Keyboard and Mouse

Nagtilak

2024

IJSREM

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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

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“…The 15 activity features include: 1) mean, 2) standard deviation (std), 3) variance (var), 4) information entropy (ent), 5) amplitude area (area), 6) root mean square (rms), 7) mean absolute error (MAE), 8) maximum (max), 9) minimum (min), 10) median (med), 11) mode, 12) range, 13) intermediate range (mid), 14) interquartile range (IQR), and 15) energy (EN). [34][35][36][37] In addition, the maximum-minimum normalization method is used to normalize the feature value to eliminate the influence of unit and scale differences between features [0,1].…”

Section: Second-level Activity Recognitionmentioning

confidence: 99%

A Single Smart Ring for Monitoring 20 Kinds of Multi‐Intensity Daily Activities––From Kitchen Work to Fierce Exercises

Zhao

Liu

Lian

et al. 2022

Advanced Intelligent Systems

Self Cite

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Statistical analysis of human daily activities contributes to time planning and health management. It also helps people make healthcare judgments and disease predictions when combined with big data technology. However, traditional methods for recognition of human daily activities based on vision and multisensors have limitations due to poor portability and weak capability in multiple activities detection. Herein, the development of a wearable human activity recognition (HAR) smart ring capable of recognizing at least 20 multi‐intensity activities, i.e., motions ranging from clean‐the‐desk to playing basketball, based on a microinertial measurement unit and a hierarchical decision algorithm is proposed. Users only need to wear the smart ring on the index finger of the right hand to accurately identify 20 common activities that are classified as light, vigorous, and fierce. A novel hierarchical decision algorithm using 70 features is proposed to improve the accuracy and speed of recognizing common human activities and provides a final recognition accuracy of 98.10% for 20 types of activities. This extremely portable, reliable, and high‐accuracy HAR solution is a significant advancement in providing real‐time quantitative data for personal lifestyle supervision, time planning, and healthcare management.

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Towards a Virtual Keyboard Scheme Based on Wearing One Motion Sensor Ring on Each Hand

Cited by 15 publications

References 36 publications

Application of Heart Rate Combined with Acceleration Motion Sensor in Sports Dance Teaching

Application of Heart Rate Combined with Acceleration Motion Sensor in Sports Dance Teaching

Survey on Gesture-Based Virtual Keyboard and Mouse

A Single Smart Ring for Monitoring 20 Kinds of Multi‐Intensity Daily Activities––From Kitchen Work to Fierce Exercises

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