scholarly journals Recognizing Full-Body Exercise Execution Errors Using the Teslasuit

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8389
Author(s):  
Polona Caserman ◽  
Clemens Krug ◽  
Stefan Göbel
Keyword(s):  
Motion Capture ◽  
Motor Skills ◽  
Haptic Feedback ◽  
Inertial Sensors ◽  
Body Part ◽  
Body Motion ◽  
Electrical Nerve Stimulation ◽  
Movement Assessment ◽  
Regular Physical Exercise ◽  
Full Body

Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements.

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.

scholarly journals An optimization-based approach to human body motion capture using inertial sensors

2014 ◽  
Vol 47 (3) ◽  
pp. 79-85 ◽  
Author(s):  
Manon Kok ◽  
Jeroen D. Hol ◽  
Thomas B. Schön
Keyword(s):  
Motion Capture ◽  
Human Body ◽  
Inertial Sensors ◽  
Body Motion

scholarly journals Leveraging Two Kinect Sensors for Accurate Full-Body Motion Capture

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 24297-24317 ◽  
Author(s):  
Zhiquan Gao ◽  
Yao Yu ◽  
Yu Zhou ◽  
Sidan Du
Keyword(s):  
Motion Capture ◽  
Body Motion ◽  
Full Body

scholarly journals A Hybrid Tracking System of Full-Body Motion Inside Crowds

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2108
Author(s):  
Maik Boltes ◽  
Juliane Adrian ◽  
Anna-Katharina Raytarowski
Keyword(s):  
Motion Tracking ◽  
Inertial Sensors ◽  
Tracking System ◽  
Body Motion ◽  
Whole Body ◽  
Body Parts ◽  
Movement Velocity ◽  
Camera System ◽  
Pedestrian Dynamics ◽  
Full Body

For our understanding of the dynamics inside crowds, reliable empirical data are needed, which could enable increases in safety and comfort for pedestrians and the design of models reflecting the real dynamics. A well-calibrated camera system can extract absolute head position with high accuracy. The inclusion of inertial sensors or even self-contained full-body motion capturing systems allows the relative tracking of invisible people or body parts or capturing the locomotion of the whole body even in dense crowds. The newly introduced hybrid system maps the trajectory of the top of the head coming from a full-body motion tracking system to the head trajectory of a camera system in global space. The fused data enable the analysis of possible correlations of all observables. In this paper we present an experiment of people passing though a bottleneck and show by example the influences of bottleneck width and motivation on the overall movement, velocity, stepping locomotion and rotation of the pelvis. The hybrid tracking system opens up new possibilities for analyzing pedestrian dynamics inside crowds, such as the space requirement while passing through a bottleneck. The system allows linking any body motion to characteristics describing the situation of a person inside a crowd, such as the density or movements of other participants nearby.

scholarly journals A Pilot Study Quantifying Center of Mass Trajectory during Dynamic Balance Tasks Using an HTC Vive Tracker Fixed to the Pelvis

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8034
Author(s):  
Susanne M. van der Veen ◽  
James S. Thomas
Keyword(s):  
Motion Capture ◽  
Dynamic Balance ◽  
Center Of Mass ◽  
Berg Balance Scale ◽  
Body Motion ◽  
Average Error ◽  
Balance Scale ◽  
Average Error Rate ◽  
Fall Rates ◽  
Full Body

Fall rates are increasing among the aging population and even higher falls rates have been reported in populations with neurological impairments. The Berg Balance Scale is often used to assess balance in older adults and has been validated for use in people with stroke, traumatic brain injury, and Parkinson’s disease. While the Berg Balance Scale (BBS) has been found to be predictive of the length of rehabilitation stay following stroke, a recent review concluded the BBS lacked predictive validity for fall risk. Conversely, sophisticated measures assessing center of mass (COM) displacement have shown to be predictive of falls risk. However, calculating COM displacement is difficult to measure outside a laboratory. Accordingly, we sought to validate COM displacement measurements derived from an HTC Vive tracker secured to the pelvis by comparing it to COM derived from ‘gold’ standard laboratory-based full-body motion capture. Results showed that RMS between the COM calculated from HTC Vive tracker and full body motion capture agree with an average error rate of 2.1 ± 2.6 cm. Therefore, we conclude measurement of COM displacement using an HTC Vive tracker placed on the pelvis is reasonably representative of laboratory-based measurement of COM displacement.

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