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.

Validation of a low-cost inertial motion capture system for whole-body motion analysis

2020 ◽  
Vol 99 ◽  
pp. 109520 ◽  
Author(s):  
X. Robert-Lachaine ◽  
H. Mecheri ◽  
A. Muller ◽  
C. Larue ◽  
A. Plamondon
Keyword(s):  
Motion Analysis ◽  
Motion Capture ◽  
Low Cost ◽  
Body Motion ◽  
Whole Body ◽  
Motion Capture System ◽  
Inertial Motion

scholarly journals Development of Underwater Motion Capture System for Space Suit Mobility Assessment

2017 ◽  
Vol 61 (1) ◽  
pp. 945-949 ◽  
Author(s):  
Yaritza Bernal ◽  
K. Han Kim ◽  
Elizabeth Benson ◽  
Sarah Jarvis ◽  
Lauren Harvill ◽  
...  
Keyword(s):  
Motion Capture ◽  
Whole Body ◽  
Microgravity Environment ◽  
Motion Capture System ◽  
Space Suit ◽  
Neutral Buoyancy ◽  
Dry Land ◽  
Mobility Assessment ◽  
Space Suits ◽  
Land Condition

The objective of this study was to develop and deploy a novel motion capture system that utilizes off-the-shelf, dive-rated hardware to measure 3-D whole body reach envelopes of space suits in an underwater analog, which simulates a microgravity environment. The accuracy of the developed system was compared to a gold standard motion capture system in a dry-land condition before deployment. This study is ultimately aimed at providing a methodology for quantitative metrics to evaluate and compare the mobility performances of a newly developed prototype space suit versus an existing space suit at the Neutral Buoyancy Laboratory (NBL) at NASA’s Johnson Space Center.

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 Low-Dimensional Dissimilarity Analysis of Unilateral and Bilateral Stroke-Impacted Hand Trajectories

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Jay Ryan U. Roldan ◽  
Dejan Milutinović ◽  
Zhi Li ◽  
Jacob Rosen
Keyword(s):  
Multidimensional Scaling ◽  
Healthy Subject ◽  
Motion Capture ◽  
Early Phase ◽  
Three Dimensional ◽  
High Rate ◽  
Motion Capture System ◽  
Trajectory Data ◽  
The Difference ◽  
Low Dimensional

In this paper, we propose a quantitative approach based on identifying hand trajectory dissimilarities through the use of a multidimensional scaling (MDS) analysis. A high-rate motion capture system is used to gather three-dimensional (3D) trajectory data of healthy and stroke-impacted hemiparetic subjects. The mutual dissimilarity between any two trajectories is measured by the area between them. This area is used as a dissimilarity variable to create an MDS map. The map reveals a structure for measuring the difference and variability of individual trajectories and their groups. The results suggest that the recovery of hemiparetic subjects can be quantified by comparing the difference and variability of their individual MDS map points to the points from the cluster of healthy subject trajectories. Within the MDS map, we can identify fully recovered patients, those who are only functionally recovered, and those who are either in an early phase of, or are nonresponsive to the therapy.

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