Martinez-Hernandez, U., Rubio-Solis, A. and Dehghani-Sanij, A.A. (2018) Recognition of walking activity and prediction of gait periods with a CNN and first-order MC strategy. In: Proceedings of the IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), 26-29 Aug 2018, Enschede, Netherlands. IEEE , pp. 897-902. ISBN 9781538681831
Abstract
In this paper, a strategy for recognition of human walking activities and prediction of gait periods using wearable sensors is presented. First, a Convolutional Neural Network (CNN) is developed for the recognition of three walking activities (level-ground walking, ramp ascent and descent) and recognition of gait periods. Second, a first-order Markov Chain (MC) is employed for the prediction of gait periods, based on the observation of decisions made by the CNN for each walking activity. The validation of the proposed methods is performed using data from three inertial measurement units (IMU) attached to the lower limbs of participants. The results show that the CNN, together with the first-order MC, achieves mean accuracies of 100% and 98.32% for recognition of walking activities and gait periods, respectively. Prediction of gait periods are achieved with mean accuracies of 99.78%, 97.56% and 97.35% during level-ground walking, ramp ascent and descent, respectively. Overall, the benefits of our work for accurate recognition and prediction of walking activity and gait periods, make it a suitable high-level method for the development of intelligent assistive robots.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Legged locomotion; Robot sensing systems; Convolution; Foot; Wearable sensors; Predictive models; Accelerometers |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Nov 2018 15:12 |
Last Modified: | 27 Nov 2018 15:12 |
Published Version: | https://doi.org/10.1109/BIOROB.2018.8487220 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/BIOROB.2018.8487220 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139218 |