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Volume 4 Issue 4
Oct.  2017

IEEE/CAA Journal of Automatica Sinica

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Zuojun Liu, Wei Lin, Yanli Geng and Peng Yang, "Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 651-660, Oct. 2017. doi: 10.1109/JAS.2017.7510619
Citation: Zuojun Liu, Wei Lin, Yanli Geng and Peng Yang, "Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 651-660, Oct. 2017. doi: 10.1109/JAS.2017.7510619

Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors

doi: 10.1109/JAS.2017.7510619

the National Nature Science Fundation 61174009

the National Nature Science Fundation 61203323

Youth Foundation of Hebei Province F2016202327

the Colleges and Universities in Hebei Province Science and Technology Research Project ZC2016020

Wei Lin's work was supported in part by Key Project of NSFC 61533009

111 Project B08015

Research Project JCYJ20150403161923519

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  • 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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