scholarly journals A Modular Data Glove System for Finger and Hand Motion Capture Based on Inertial Sensors

2018 ◽  
Vol 39 (4) ◽  
pp. 532-540 ◽  
Author(s):  
Bor-Shing Lin ◽  
I-Jung Lee ◽  
Pei-Ying Chiang ◽  
Shih-Yuan Huang ◽  
Chih-Wei Peng
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Data Glove ◽  
Hand Motion ◽  
Modular Data

scholarly journals A new IMMU-based data glove for hand motion capture with optimized sensor layout

2019 ◽  
Vol 3 (1) ◽  
pp. 19-32 ◽  
Author(s):  
Quan Liu ◽  
Guoming Qian ◽  
Wei Meng ◽  
Qingsong Ai ◽  
Chaoyue Yin ◽  
...  
Keyword(s):  
Motion Capture ◽  
Data Glove ◽  
Hand Motion

Design of a Wireless 3D Hand Motion Tracking and Gesture Recognition Glove for Virtual Reality Applications

2017 ◽  
Author(s):  
Jen-Hsuan Hsiao ◽  
Yu-Heng Deng ◽  
Tsung-Ying Pao ◽  
Hsin-Rung Chou ◽  
Jen-Yuan (James) Chang
Keyword(s):  
Virtual Reality ◽  
Gesture Recognition ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Computer Control ◽  
Recognition Algorithm ◽  
Training Time ◽  
Analog To Digital ◽  
Data Glove ◽  
Hand Motion

Hand motion tracking and gesture recognition are of crucial interest to the development of virtual reality systems and controllers. In this paper, a wireless data glove that can accurately sense hands’ dynamic movements and gestures of different modes was proposed. This data glove was custom-built, consisting of flex and inertial sensors, and a microcontroller with multi-channel ADC (analog to digital converter). For the classification algorithm, a hierarchical gesture system using Naïve Bayes Classifier was built. This low training time recognition algorithm allows categorization of all input signals, such as clicking, pointing, dragging, rotating and switching functions when performing computer control. This glove provided a more intuitive way to operate with human-computer interface. Some preliminary experimental results were presented in this paper. The data glove was also operated as a controller in a First-Person Shooter (FPS) game to perform the usability of the proposed glove.

A Mobile Motion Capture System Based on Inertial Sensors and Smart Shoes

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Pyeong-Gook Jung ◽  
Sehoon Oh ◽  
Gukchan Lim ◽  
Kyoungchul Kong
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Health Condition ◽  
Body Motion ◽  
Whole Body ◽  
Motion Capture System ◽  
Outdoor Sports ◽  
Motion Capture Systems

Motion capture systems play an important role in health-care and sport-training systems. In particular, there exists a great demand on a mobile motion capture system that enables people to monitor their health condition and to practice sport postures anywhere at any time. The motion capture systems with infrared or vision cameras, however, require a special setting, which hinders their application to a mobile system. In this paper, a mobile three-dimensional motion capture system is developed based on inertial sensors and smart shoes. Sensor signals are measured and processed by a mobile computer; thus, the proposed system enables the analysis and diagnosis of postures during outdoor sports, as well as indoor activities. The measured signals are transformed into quaternion to avoid the Gimbal lock effect. In order to improve the precision of the proposed motion capture system in an open and outdoor space, a frequency-adaptive sensor fusion method and a kinematic model are utilized to construct the whole body motion in real-time. The reference point is continuously updated by smart shoes that measure the ground reaction forces.

Influence of Electrode Positions on Performance of Hand Motion Capture using EIT

2021 ◽  
pp. 1-1
Author(s):  
Shunsuke Yoshimoto ◽  
Yumi Toyoda ◽  
Akio Yamamoto
Keyword(s):  
Motion Capture ◽  
Hand Motion

Hand Motion Capture

2008 ◽  
pp. 137-150 ◽  
Author(s):  
Midori Kitagawa ◽  
Brian Windsor
Keyword(s):  
Motion Capture ◽  
Hand Motion

Hand Motion Capture

2012 ◽  
pp. 137-150
Author(s):  
Midori Kitagawa ◽  
Brian Windsor
Keyword(s):  
Motion Capture ◽  
Hand Motion

scholarly journals Comparative Analysis of Inertial Sensor to Optical Motion Capture System Performance in Push-Pull Exertion Postures

2016 ◽  
Vol 60 (1) ◽  
pp. 970-974 ◽  
Author(s):  
Sol Lim ◽  
Andrea Case ◽  
Clive D’Souza
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Interactive Effects ◽  
Flexion Angle ◽  
Task Demand ◽  
Optical Motion ◽  
Physical Task ◽  
Target Force ◽  
Optical Motion Capture

This study examined interactions between inertial sensor (IS) performance and physical task demand on posture kinematics in a two-handed force exertion task. Fifteen male individuals participated in a laboratory experiment that involved exerting a two-handed isometric horizontal force on an instrumented height-adjustable handle. Physical task demand was operationalized by manipulating vertical handle height, target force magnitude, and force direction. These factors were hypothesized to influence average estimates of torso flexion angle measured using inertial sensors and an optical motion capture (MC) system, as well as the root mean squared errors (RMSE) between instrumentation computed over a 3s interval of the force exertion task. Results indicate that lower handle heights and higher target force levels were associated with increased torso and pelvic flexion in both, push and pull exertions. Torso flexion angle estimates obtained from IS and MC did not differ significantly. However, RMSE increased with target force intensity suggesting potential interactive effects between measurement error and physical task demand.

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