In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs

Nur Hidayah Mohd Yusof; Nur Azah Hamzaid; Farahiyah Jasni; Khin Wee Lai

doi:10.1117/1.JEI.28.2.021002

31 December 2018 In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs

Nur Hidayah Mohd Yusof, Nur Azah Hamzaid, Farahiyah Jasni, Khin Wee Lai

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 2, 021002 (December 2018). https://doi.org/10.1117/1.JEI.28.2.021002

Abstract

An in-socket sensory system enables the monitoring of transfemoral amputee movement for a microprocessor-controlled prosthetic leg. User movement recognition from an in-socket sensor allows a powered prosthetic leg to actively mimic healthy ambulation, thereby reducing an amputee’s metabolic energy consumption. This study established an adaptive neurofuzzy inference system (ANFIS)-based control input framework from an in-socket sensor signal for gait phase classification to derive user intention as read by in-socket sensor arrays. Particular gait phase recognition was mapped with the cadence and torque control output of a knee joint actuator. The control input framework was validated with 30 experimental gait samples of the in-socket sensory signal of a transfemoral amputee walking at fluctuating speeds of 0 to 2 km · h^− 1. The physical simulation of the controller presented a realistic simulation of the actuated knee joint in terms of a knee mechanism with 95% to 99% accuracy of knee cadence and 80% to 90% accuracy of torque compared with those of normal gait. The ANFIS system successfully detected the seven gait phases based on the amputee’s in-socket sensor signals and assigned accurate knee joint torque and cadence values as output.

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Nur Hidayah Mohd Yusof, Nur Azah Hamzaid, Farahiyah Jasni, and Khin Wee Lai "In-socket sensory system with an adaptive neuro-based fuzzy inference system for active transfemoral prosthetic legs," Journal of Electronic Imaging 28(2), 021002 (31 December 2018). https://doi.org/10.1117/1.JEI.28.2.021002

Received: 23 April 2018; Accepted: 19 September 2018; Published: 31 December 2018

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