Abstract
For patients with certain types of movement disorders, electrical stimulation is used to facilitate the rehabilitation process. Typically, rehabilitation specialists manually control the triggering of the electrical pulse at desired points in the gait cycle, and this manual intervention can be erroneous. In this paper, we develop a real time gait parameter estimation model using artificial neural networks to activate electrical stimulation at a desired point in the gait cycle. The weights obtained from the neural network model are trained across a sample of similar aged population. The predicted output obtained from the real-time model were compared with the output of the offline analysis. We further compare results from a specific model trained with data of a single individual with that of a general model trained with data of eight individuals. A part of this work was funded by a BIRAC project BT/AIR0945/PACE-19/19.
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Rahman, H., Kumbla, A., Megharjun, V.N., Talasila, V. (2022). Real-Time Heel Strike Parameter Estimation for FES Triggering. In: Majhi, S., Prado, R.P.d., Dasanapura Nanjundaiah, C. (eds) Distributed Computing and Optimization Techniques. Lecture Notes in Electrical Engineering, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-19-2281-7_69
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DOI: https://doi.org/10.1007/978-981-19-2281-7_69
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