Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors |
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Authors: | Zuojun Liu Wei Lin Yanli Geng Peng Yang |
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Affiliation: | 1.School of Control Science and Engineering, Hebei University of Technology, Tianjin 300130, China2.Engineering Research Center of Intelligent Rehabilitation, Ministry of Education, Tianjin 300130, China3.Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA |
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Abstract: | Based on the regularity nature of lower-limb motion, an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram (EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient (ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model (HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground, stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis. |
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Keywords: | Above-knee prosthesis hidden Markov model (HMM) intra-class correlation coefficient (ICC) intent pattern recognition sensor fusion |
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