Human pose recovery for rehabilitation using ambulatory sensors

Author

Lin, Jonathan Feng-Shun ; Kulic, Dana

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada

fYear

2013

fDate

3-7 July 2013

Firstpage

4799

Lastpage

4802

Abstract

In this paper, an approach for lower-leg pose recovery from ambulatory sensors is implemented and validated in a clinical setting. Inertial measurement units are attached to patients undergoing physiotherapy. The sensor data is combined with a kinematic model within an extended Kalman filter framework to perform joint angle estimation. Anthropometric joint limits and process noise adaptation are employed to improve the quality of the joint angle estimation. The proposed approach is tested on 7 patients following total hip or knee joint replacement surgery. The proposed approach achieves an average root-mean-square error of 0.12 radians at key poses.

Keywords

Kalman filters; anthropometry; biomedical equipment; mean square error methods; medical signal processing; patient rehabilitation; signal denoising; surgery; wireless sensor networks; ambulatory sensors; anthropometric joint limits; average root-mean-square error; clinical setting; extended Kalman filter framework; human pose recovery; inertial measurement units; joint angle estimation; joint angle estimation quality; kinematic model; knee joint replacement surgery; lower-leg pose recovery; physiotherapy; process noise adaptation; rehabilitation; sensor data; total hip joint replacement surgery; Acceleration; Accelerometers; Gyroscopes; Joints; Kinematics; Knee; Sensors;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE

Conference_Location

Osaka

ISSN

1557-170X

Type

conf

DOI

10.1109/EMBC.2013.6610621

Filename

6610621