Abstract
Human gait analysis is an emerging area that has a wide application in medical science specially exoskeleton-based rehabilitation robots. In this paper, a linear time-series-based prediction models have been proposed for joint movement for the lower extremity. The joint movement data is collected at RAMAN Lab, MNIT Jaipur. Experimental results indicate that this approach is better than feedforward neural network in the case of linearly correlated data, considering mean absolute percentage error as an evaluation measure. The proposed prediction model could be used for efficient control of lower extremity robot-assisted device for a smooth gait for the patients.
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Acknowledgements
This work was supported by Department of Science and Technology, India; project under grant SR/S3/MERC/0101/2012.
Declaration The authors have obtained all ethical approvals from appropriate ethical committee and written approval from the subjects involved in this study.
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Prakash, C., Sujil, A., Kumar, R., Mittal, N. (2019). Linear Prediction Model for Joint Movement of Lower Extremity. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 707. Springer, Singapore. https://doi.org/10.1007/978-981-10-8639-7_24
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DOI: https://doi.org/10.1007/978-981-10-8639-7_24
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