Implementation of Inertial Sensor based Human Body Motion Capture System for the Self-Coaching

2014 ◽  
Vol 12 (4) ◽  
Author(s):  
Kyeong-Ri Ko
Keyword(s):  
Motion Capture ◽  
Human Body ◽  
Inertial Sensor ◽  
The Self ◽  
Body Motion ◽  
Motion Capture System

Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

2022 ◽  
pp. 095441192110729
Author(s):  
Gunjan Patel ◽  
Rajani Mullerpatan ◽  
Bela Agarwal ◽  
Triveni Shetty ◽  
Rajdeep Ojha ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Analysis ◽  
Motion Capture ◽  
Inertial Sensor ◽  
Joint Kinematics ◽  
Percentage Error ◽  
Motion Capture System ◽  
Spatiotemporal Parameters ◽  
Analysis System ◽  
Motion Capture Systems

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

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.

Validity of the Perception Neuron inertial motion capture system for upper body motion analysis

Measurement ◽  
2020 ◽  
Vol 149 ◽  
pp. 107024 ◽  
Author(s):  
Ryan Sers ◽  
Steph Forrester ◽  
Esther Moss ◽  
Stephen Ward ◽  
Jianjia Ma ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Motion Capture ◽  
Body Motion ◽  
Upper Body ◽  
Motion Capture System ◽  
Inertial Motion

Human Body Motion Capture via 3D Graph-cuts

2006 ◽  
Author(s):  
Chengkai Wan ◽  
Baozong Yuan ◽  
Yunda Sun ◽  
Zhenjiang Miao
Keyword(s):  
Motion Capture ◽  
Human Body ◽  
Graph Cuts ◽  
Body Motion
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