Martinez-Hernandez, U orcid.org/0000-0002-9922-7912, Mahmood, I and Dehghani-Sanij, AA (2017) Probabilistic locomotion mode recognition with wearable sensors. In: Ibáñez, J, Gonzalez-Vargas, J, Azorín, JM, Akay, M and Pons, JL, (eds.) Converging Clinical and Engineering Research on Neurorehabilitation II. 3rd International Conference on NeuroRehabilitation (ICNR2016), 18-21 Oct 2016, Segovia, Spain. Biosystems & Biorobotics (15). Springer , Cham, Switzerland , pp. 1037-1042. ISBN 978-3-319-46668-2
Abstract
Recognition of locomotion mode is a crucial process for control of wearable soft robotic devices to assist humans in walking activities. We present a probabilistic Bayesian approach with a sequential analysis method for recognition of locomotion and phases of the gait cycle. Our approach uses recursive accumulation of evidence, as biological systems do, to reduce uncertainty present in the sensor measurements, and thus improving recognition accuracy. Data were collected from a wearable sensor, attached to the shank of healthy human participants, from three locomotion modes; level-ground walking, ramp ascent and ramp descent. We validated our probabilistic approach with recognition of locomotion in steady-state and gait phases in transitional states. Furthermore, we evaluated the effect, in recognition accuracy, of the accumulation of evidence controlled by increasing belief thresholds. High accuracy results achieved by our approach, demonstrate its potential for robust control of lower limb wearable soft robotic devices to provide natural and safe walking assistance to humans.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/M026388/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Jul 2016 12:57 |
Last Modified: | 17 Aug 2017 13:49 |
Status: | Published |
Publisher: | Springer |
Series Name: | Biosystems & Biorobotics |
Identification Number: | 10.1007/978-3-319-46669-9_168 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:102303 |