Results 241 to 250 of about 95,088 (289)

Markerless motion capture using appearance and inertial data

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
Current monitoring techniques for biomechanical analysis typically capture a snapshot of the state of the subject due to challenges associated with long-term monitoring. Continuous long-term capture of biomechanics can be used to assess performance in the workplace and rehabilitation at home.
Charence, Wong, Zhiqiang, Zhang, Benny, Lo, Guang-Zhong, Yang   +3 more
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Inertial-based Motion Capturing and Smart Training System

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
Smart coaching platforms are emerging which combine Body-Sensor-Networks with AI-based training software to monitor and analyze body motions of athletes, workers, or medical patients. This allows for new opportunities to explore algorithms to interpret body sensor data and provide analytical feedback for learning a physical task, refining body motions,
Jens Windau, Laurent Itti
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Hand/Arm Robot Teleoperation by Inertial Motion Capture

2013 Second International Conference on Robot, Vision and Signal Processing, 2013
The multi-fingered robot hand has much attention in various fields. Many robot hands have been proposed so far and we have developed a hand/arm robot with universal robot hand II. A teleoperation system allows intuitive manipulation of the hand/arm robot.
Futoshi Kobayashi, Keiichi Kitabayashi, Hiroyuki Nakamoto, Fumio Kojima   +3 more
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Machine Learning for Placement-Insensitive Inertial Motion Capture

2018 IEEE International Conference on Robotics and Automation (ICRA), 2018
Although existing inertial motion-capture systems work reasonably well (≤10° error in Euler angles), their accuracy suffers when sensor positions change relative to the associated body segments (±60° mean error and 120° standard deviation). We attribute this performance degradation to undermined calibration values, sensor movement latency and ...
Xuesu Xiao, Shuayb Zarar
openaire   +1 more source

Motion capture based identification of the human body inertial parameters

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008
Identification of body inertia, masses and center of mass is an important data to simulate, monitor and understand dynamics of motion, to personalize rehabilitation programs. This paper proposes an original method to identify the inertial parameters of the human body, making use of motion capture data and contact forces measurements.
Gentiane, Venture, Ko, Ayusawa, Yoshihiko, Nakamura   +2 more
openaire   +2 more sources