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IMU-Based Gait Phase Recognition for Stroke Survivors

Published online by Cambridge University Press:  10 April 2019

Yu Lou ,
Rongli Wang ,
Jingeng Mai ,
Ninghua Wang  and
Qining Wang Open the ORCID record for Qining Wang [Opens in a new window]
Yu Lou
Affiliation:
The Robotics Research Group, College of Engineering, Peking University, Beijing 100871, China. E-mails: [email protected], [email protected] Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China
Rongli Wang
Affiliation:
Department of Rehabilitation Medicine, First Hospital, Peking University, Beijing 100034, China. E-mails: [email protected], [email protected] Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China
Jingeng Mai
Affiliation:
The Robotics Research Group, College of Engineering, Peking University, Beijing 100871, China. E-mails: [email protected], [email protected] Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China Beijing Innovation Center for Engineering Science and Advanced Technology (BIC-ESAT), Peking University, Beijing 100871, China
Ninghua Wang
Affiliation:
Department of Rehabilitation Medicine, First Hospital, Peking University, Beijing 100034, China. E-mails: [email protected], [email protected] Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China
Qining Wang*
Affiliation:
The Robotics Research Group, College of Engineering, Peking University, Beijing 100871, China. E-mails: [email protected], [email protected] Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China Beijing Innovation Center for Engineering Science and Advanced Technology (BIC-ESAT), Peking University, Beijing 100871, China
*
*Corresponding author. E-mail: [email protected]
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Summary

Using wearable robots is an effective means of rehabilitation for stroke survivors, and effective recognition of human motion intentions is a key premise in controlling wearable robots. In this paper, we propose an inertial measurement unit (IMU)-based gait phase detection system. The system consists of two IMUs that are tied on the thigh and on the shank, respectively, for collecting acceleration and angular velocity. Features were extracted using a sliding window of 150 ms in length, which was then fed into a quadratic discriminant analysis (QDA) classifier for classification. We recruited five stroke survivors to test our system. They walked at their own preferred speed on the level ground. Experimental results show that our proposed system has the ability of recognizing the gait phase of stroke survivors. All recognition accuracy results are above 96.5%, and detections are about 5–15 ms in advance of time. In addition, using only one IMU can also give reliable recognition results. This paper proposes an idea about the further research on human–computer interaction for the control of wearable robots.

Keywords

ExoskeletonsHuman biomechanicsMan–machine systemsMechatronic systemsControl of robotic systems
Type
Articles
Information
Robotica , Volume 37 , Special Issue 12: Wearable Robotics: Dynamics, Control and Applications , December 2019 , pp. 2195 - 2208
Copyright
© Cambridge University Press 2019 

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Footnotes

Yu Lou and Rongli Wang contributed equally to this paper.

References

Mackay, J., Mensah, G. A., Mendis, S. and Greenlund, K., “The atlas of heart disease and stroke: World Health Organization” (2004).Google Scholar
von Schroeder, H. P., et al., “Gait parameters following stroke: A practical assessment,” J. Rehabil. Res. Develop. 32(1), 25–31 (1995).Google ScholarPubMed
Wade, D. T., et al., “Walking after stroke. Measurement and recovery over the first 3 months,” Scand. J. Rehabil. Med. 19(1), 25–30 (1987).Google ScholarPubMed
Mohammed, S., Amirat, Y. and Rifai, H., “Lower-limb movement assistance through wearable robots: State of the art and challenges,” Adv. Robot. 26(1), 1–22 (2012).CrossRefGoogle Scholar
Colombo, G., Joerg, M., Schreier, R. and Dietz, V., “Treadmill training of paraplegic patients using a robotic orthosis,” J. Rehabil. Res. Develop. 37(6), 693–700 (2000).Google ScholarPubMed
Hesse, S. and Uhlenbrock, D., “A mechanized gait trainer for restoration of gait,” J. Rehab. Res. Develop. 37(6), 701–708 (2000).Google Scholar
Burgess, J. K., Weibel, G. C. and Brown, D. A., “Overground walking speed changes when subjected to body weight support conditions for nonimpaired and post stroke individuals,” J. Neuroeng. Rehabil. 7(1), 6–16 (2010).CrossRefGoogle ScholarPubMed
Patterson, K. K., et al., “Evaluation of gait symmetry after stroke: A comparison of current methods and recommendations for standardization,” Gait Posture. 31(2), 241–246 (2010).CrossRefGoogle ScholarPubMed
Kotiadis, D., Hermens, H. J. and Veltink, P. H., “Inertial gait phase detection for control of a drop foot stimulator,” Med. Eng. Phys. 32(4), 287–297 (2010).CrossRefGoogle ScholarPubMed
https://www.strokengine.ca/assess/fac/ Google Scholar
Jung, J. Y., Heo, W., Yang, H. and Park, H., “A neural network-based gait phase classification method using sensors equipped on lower limb exoskeleton robots,” Sensors. 15(11), 27738–27759 (2015).CrossRefGoogle ScholarPubMed
Mariani, B., Rouhani, H., Crevoisier, X. and Aminian, K., “Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors,” Gait Posture. 37(2), 229–234 (2013).Google ScholarPubMed
Aung, M. S., Thies, S. B., Kenney, L. P., Howard, D., Selles, R. W., Findlow, A. H. and Goulermas, J. Y., “Automated detection of instantaneous gait events using time frequency analysis and manifold embedding,” IEEE Trans. Neural Syst. Rehabil. Eng. 21(6), 908–916 (2013).CrossRefGoogle ScholarPubMed
Mannini, A. and Sabatini, A. M., “Gait phase detection and discrimination between walkingCjogging activities using hidden Markov models applied to foot motion data from a gyroscope,” Gait Posture. 36(4), 657–661 (2012).CrossRefGoogle ScholarPubMed
Jiang, X., Chu, K. H., Khoshnam, M. and Menon, C., “A wearable gait phase detection system based on force myography techniques,” Sensors. 18(4), 1279–1291 (2018).CrossRefGoogle ScholarPubMed
Kong, K. and Tomizuka, M., “A gait monitoring system based on air pressure sensors embedded in a shoe,” IEEE/ASME Trans. Mechatron. 14(3), 358–370 (2009).Google Scholar
Attal, F., Amirat, Y., Chibani, A. and Mohammed, S., “Automatic recognition of gait phases using a multiple regression hidden Markov model,” IEEE/ASME Trans. Mechatron. 23(4), 1597–1607 (2018).Google Scholar
Crea, S., Donati, M., De Rossi, S. M. M., Oddo, C. M. and Vitiello, N., “A wireless flexible sensorized insole for gait analysis,” Sensors. 14(1), 1073–1093 (2014).CrossRefGoogle ScholarPubMed
Joshi, C. D., et al., “Classification of Gait Phasesfrom Lower Limb EMG: Application to Exoskeleton Orthosis,” Proc. IEEE-EMBS Special Topic Conf. Point-of-Care Healthc. Technol., 228C–231 (2013).Google Scholar
Lee, S. W., Yi, T., Jung, J. W. and Bien, Z., “Design of a gait phase recognition system that can cope with EMG electrode location variation,” IEEE Trans. Autom. Sci. Eng. 14(3), 1429–1439 (2017).CrossRefGoogle Scholar
Ryu, J. and Kim, D. H., “Real-time gait subphase detection using an EMG signal graph matching (ESGM) algorithm based on EMG signals,” Expert Syst. Appl. 85(1), 357–365 (2017).Google Scholar
Balaban, B. and Tok, F., “Gait disturbances in patients with stroke,” PMR. 6(7), 635–642, (2014).CrossRefGoogle ScholarPubMed
Chen, B., Wang, X., Huang, Y., Wei, K. and Wang, Q., “A foot-wearable interface for locomotion mode recognition based on discrete contact force distribution,” Mechatronics, 32, 12–21 (2015).CrossRefGoogle Scholar