DocumentCode :
104745
Title :
Adaptive Interface for Personalized Center of Mass Self-Identification in Home Rehabilitation
Author :
Gonzalez, Alejandro ; Fraisse, Philippe ; Hayashibe, Mitsuhiro
Author_Institution :
LIRMM, Univ. of Montpellier, Montpellier, France
Volume :
15
Issue :
5
fYear :
2015
fDate :
May-15
Firstpage :
2814
Lastpage :
2823
Abstract :
As the center of mass (CoM) position can be used to determine stability, current rehabilitation standards may be improved by tracking it. A personalized CoM estimate can be obtained using the statically equivalent serial chain (SESC) once the model parameters are identified. The identification phase can be completed using low-cost sensors (Kinect and Wii balance board) outside the laboratory making CoM estimation feasible in a patient´s home. This paper focuses on: 1) improving the SESC identification quality and speed and 2) using the estimated CoM to determine stability. Identification time is reduced by creating a visual adaptive interface where the subject´s limbs are colored based on the convergence of the SESC parameters. A study was conducted on eight subjects and showed a faster convergence and lower root mean square error (RMSE) when the adaptive interface was used. We found that a model capable of estimating the CoM position with an RMSE of 27 mm could be obtained after only 90 s of identification when the interface was used, whereas twice as much time was needed when the interface was not used. The interface that was developed can be used by a subject to track his/her CoM position in a self-directed way. Stability was determined for a squat task using a dynamic index obtained from the estimated CoM trajectory and using only Kinect measurements. This shows one potential application for home rehabilitation and monitoring.
Keywords :
mean square error methods; patient monitoring; patient rehabilitation; statistical analysis; Kinect measurements; RMSE method; SESC identification quality; SESC parameters; adaptive interface; dynamic index; home monitoring; home rehabilitation; low root mean square error method; low-cost sensors; model parameters; personalized CoM estimation; personalized center of mass self-identification; phase identification; squat task; statiscally equivalent serial chain; visual adaptive interface; Estimation; Kalman filters; Sensors; Skeleton; Training; Trajectory; Visualization; Center of mass; Kalman filter; adaptive identification; biomechanics; home rehabilitation; postural stability; real time feedback; subject-specific modeling;
fLanguage :
English
Journal_Title :
Sensors Journal, IEEE
Publisher :
ieee
ISSN :
1530-437X
Type :
jour
DOI :
10.1109/JSEN.2014.2379431
Filename :
6994746
Link To Document :
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