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Sensors 2014, 14(7), 12349-12369; doi:10.3390/s140712349

A Locomotion Intent Prediction System Based on Multi-Sensor Fusion

Intelligent Control Laboratory, College of Engineering, Peking University, Beijing 100871, China
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Received: 30 April 2014 / Revised: 4 July 2014 / Accepted: 7 July 2014 / Published: 10 July 2014
(This article belongs to the Section Physical Sensors)
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Abstract

Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers.
Keywords: locomotion mode recognition; locomotion transition detection; sensor fusion; linear discriminant analysis; lower-limb prosthesis locomotion mode recognition; locomotion transition detection; sensor fusion; linear discriminant analysis; lower-limb prosthesis
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Chen, B.; Zheng, E.; Wang, Q. A Locomotion Intent Prediction System Based on Multi-Sensor Fusion. Sensors 2014, 14, 12349-12369.

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