NDLI: Motion intent recognition for control of a lower extremity assistive device (LEAD)

Content Provider	IEEE Xplore Digital Library
Author	Bingquan Shen Jinfu Li Fengjun Bai Chee-Meng Chew
Copyright Year	2013
Description	Author affiliation: Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore, Singapore (Bingquan Shen; Jinfu Li; Fengjun Bai; Chee-Meng Chew)
Abstract	This paper presents a motion intent recognition method to control a wearable lower extremity assistive device (LEAD) intended to aid stroke patient during activities of daily living (ADL) or rehabilitation. The main goal is to identify user's intended motion based on sensor readings from the limb attached to the assistive device, so as to execute the right control actions to aid the user in his intended action effectively. A database of a healthy subject performing various motion tasks is collected. Subsequently, the features of the signals are extracted and Principal Component Analysis (PCA) is performed to reduce the number of dimensions. Using the transformed signal, a multi-class Support Vector Machine (SVM) with Radial Basis function (RBF) kernel is trained to classify the different motion patterns. A Nelder-Mead optimization algorithm is used select the appropriate parameters for each SVM. Test results shows that the SVM can correctly classify each motion pattern with an average accuracy rate of 95.8±4.1%. An offline classification result of a healthy subject performing a series of motion task while wearing the LEAD shows that the proposed method can effectively recognize different motion intent of the user.
Starting Page	926
Ending Page	931
File Size	929399
Page Count	6
File Format	PDF
ISBN	9781467355575
e-ISBN	9781467355605
DOI	10.1109/ICMA.2013.6618039
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Legged locomotion Joints Exoskeletons Switches Dynamics Principal component analysis pattern recognition Assistive device lower extremities rehabilitation intention detection
Content Type	Text
Resource Type	Article

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