NDLI: Early detection of sit-to-stand transitions in a lower limb orthosis

Content Provider	IEEE Xplore Digital Library
Author	Bell, J. Xiangrong Shen Sazonov, E.
Copyright Year	2015
Description	Author affiliation: Dept. of Mech. Eng., Univ. of Alabama, Tuscaloosa, AL, USA (Xiangrong Shen) \|\| Dept. of Electr. & Comput. Eng., Univ. of Alabama, Tuscaloosa, AL, USA (Bell, J.; Sazonov, E.)
Abstract	This paper describes a method for early detection of posture transitions in a powered orthotic brace purposed for assisting elderly people in sit-to-stand transitions. The detection system is constructed using a linear Support Vector Machine classifier that is trained from features extracted from the signals produced by the sensors mounted on the orthosis. The sensors include accelerometers, potentiometers, gyroscopes and force sensors. The data were collected from 9 healthy individuals that performed various activities, including transitions between sitting, standing and walking. The collected sensor data were processed, normalized and manually annotated to label all posture transitions. A Support Vector Machine classifier was trained and validated in a leave-one-out manner to detect early onset of sit-to-stand transitions. Use of lagged windows was examined in efforts to increase accuracy. The proposed method was to detect posture transitions with a high detection rate and a relatively low number of false positives.
Starting Page	5028
Ending Page	5031
File Size	795337
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319521
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensors Potentiometers Knee Support vector machines Feature extraction Orthotics Legged locomotion powered orthosis intent recognition posture transitions
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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