Abstract
Locomotion mode recognition plays an important role in the control of lower-limb exoskeletons and prostheses. In such applications, the accurate and timely classification of the locomotion mode, using the minimum number of sensors, is still a challenge. In this paper we present an algorithm to recognize four different locomotion modes (namely stand, walk, stair ascent, and stair descent) and all the possible transitions among them, based on wearable sensors. The algorithm grounds on an event-based and mode-dependent strategy, which is able to recognize the locomotion mode during the swing phase. Tests conducted with three healthy subjects showed an average recognition accuracy of 98.8 ± 0.4% in steady locomotion conditions. Transitions between different modes were also accurately detected during the swing phase. Further studies will be conducted to validate the algorithm and test it in real-time applications with wearable robots.
This work was supported in part by the EU within the CYBERLEGs Plus Plus project (H2020-ICT-2016-1 Grant Agreement #731931) and in part by the Italian National Institute for Insurance against Accidents at Work (INAIL Centro Protesi, Budrio) within the MOTU project.
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References
Huo, W., Mohammed, S., Moreno, J.C., Amirat, Y.: Lower limb wearable robots for assistance and rehabilitation: a state of the art. IEEE Syst. J. 10, 1068–1081 (2016)
Parri, A., Yuan, K., Marconi, D., Yan, T., Crea, S., Munih, M., et al.: Real-time hybrid locomotion mode recognition for lower limb wearable robots. IEEE/ASME Trans. Mech. 22, 2480–2491 (2017)
Madgwick, S.: An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io and University of Bristol (UK), vol. 25 (2010)
Crea, S., Donati, M., De Rossi, S.M.M., Oddo, C.M., Vitiello, N.: A wireless flexible sensorized insole for gait analysis. Sensors 14, 1073–1093 (2014)
Chen, B., Zheng, E., Wang, Q.: A locomotion intent prediction system based on multi-sensor fusion. Sens. (Basel) 14, 12349–12369 (2014)
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Chen, B., Papapicco, V., Parri, A., Crea, S., Munih, M., Vitiello, N. (2019). A Preliminary Study on Locomotion Mode Recognition with Wearable Sensors. In: Masia, L., Micera, S., Akay, M., Pons, J. (eds) Converging Clinical and Engineering Research on Neurorehabilitation III. ICNR 2018. Biosystems & Biorobotics, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-01845-0_130
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DOI: https://doi.org/10.1007/978-3-030-01845-0_130
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