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

Zuojun Liu; Wei Lin; Yanli Geng; Peng Yang

doi:10.1109/JAS.2017.7510619

Volume 4 Issue 4

Oct. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(4): 651-660

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

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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

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

doi: 10.1109/JAS.2017.7510619

Zuojun Liu^{1, 2
,
,},
Wei Lin^3
,,
Yanli Geng^1
,,
Peng Yang^1
,

1.
School of Control Science and Engineering, Hebei University of Technology, Tianjin 300130, China
2.
Engineering Research Center of Intelligent Rehabilitation, Ministry of Education, Tianjin 300130, China
3.
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA

Funds:

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

More Information

Author Bio:
Zuojun Liu received the B.S.degree in the automation from Hebei University of Technology, Tianjin, China, in 1994, and M.S degree in the control theory and control engineering from Hebei University of Technology, Tianjin, China, in 2000, and B.A of English from Nankai University, Tianjin, China, in 2000, and Ph.D degree in the control theory and control engineering from Nankai University, Tianjin, China, in 2004.He completed his visiting scholar research at Nanyang Technological University, Singapore, in 2004 and CASE Western Reserve University, U.S in 2013, respectively.He is currently a Professor at the School of Control Science and Engineering, Hebei University of Technology.He is also with the Engineering Research Center of Intelligent Rehabilitation, Ministry of Education of China.His research interests include rehabilitation engineering, robotics, learning control, intelligent building and smart grid, (e-mail: liuzuojun@hebut.edu.cn)

Wei Lin received the D.Sc.and M.S.degrees in systems science and mathematics from Washington University, St.Louis, MO, USA, in 1993 and 1991, M.S.degree from Huazhong University of Science and Technology in 1986 and B.S.from Dalian University of Science and Technology in 1983, both in electrical engineering.During 1986-1989, he was a Lecturer in the Department of Mathematics, Fudan University, Shanghai, China.From 1994 to 1995, he was a Post-doctor and a Visiting Assistant Professor in Washington University.Since Spring of 1996, he has been a Professor in the Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA.His current research interests include nonlinear control, time-delay systems, homogeneous systems theory, estimation and adaptive control, renewable energy, power systems and smart grids.In these areas, he has published more than 200 papers in peer-refereed journals and conferences.
Dr.Lin was a recipient of the NSF CAREER Award, the Warren E.Rupp Endowed Professorship, and the Robert Herbold Faculty Fellow Award.He served as an Associate Editor of the IEEE Trans.on Automa.Contr.(1999-2002), a Guest Editor of the Special Issue on New Directions in Nonlinear Control in the IEEE Trans.on Automatic Control (2003), an Associate Editor of Automatica (2003-2005), an Associate Editor of Journal of Control Theory and Applications (2005-2008), a Subject Editor (2005-2010) and Associate Editor (2011-present) of the Int.J.of Robust and Nonlinear Control, (e-mail: linwei@case.edu)

Yanli Geng received the B.S.degree in the control technology and instruments from Agricultural University of Hebei, Hebei, China, in 2006, and M.S degree in the control theory and control engineering from Hebei University of Technology, Tianjin, China, in 2009, and Ph.D degree in the control theory and control engineering from Hebei University of Technology, Tianjin, China, in 2012.She is currently a Lecturer at the School of Control Science and Engineering, Hebei University of Technology.She is also with the Engineering Research Center of Intelligent Rehabilitation, Ministry of Education of China.Her research interests include rehabilitation engineering, intelligent control and robotics, (e-mail: gengyanli318813@163.com)

Peng Yang received the B.S.degree in the automation from Hebei University of Technology, Tianjin, China, in 1982, and M.S degree in the control theory and control engineering from Haerbing Institute of Technology, Heilongjiang, China, in 1986, and Ph.D degree in the electrical engineering from Hebei University of Technology, Tianjin, China, in 1998.He completed his visiting scholar research at Technical University of Munich, Germany, in 2005.He is currently a Professor at the School of Control Science and Engineering, Hebei University of Technology.He is also with the Engineering Research Center of Intelligent Rehabilitation, Ministry of Education of China.His research interests include rehabilitation engineering, intelligent control and robotics, (e-mail: yphebu@aliyun.com)
Corresponding author: Zuojun Liu, e-mail:liuzuojun@hebut.edu.cn
Received Date: 2016-05-29
Accepted Date: 2017-01-16

Abstract

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.
- Above-knee prosthesis,
- hidden Markov model (HMM),
- intra-class correlation coefficient (ICC),
- intent pattern recognition,
- sensor fusion

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References(34)

References

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

doi: 10.1109/JAS.2017.7510619

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