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      Smart Data-Driven Optimization of Powered Prosthetic Ankles Using Surface Electromyography

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          Abstract

          The advent of powered prosthetic ankles provided more balance and optimal energy expenditure to lower amputee gait. However, these types of systems require an extensive setup where the parameters of the ankle, such as the amount of positive power and the stiffness of the ankle, need to be setup. Currently, calibrations are performed by experts, who base the inputs on subjective observations and experience. In this study, a novel evidence-based tuning method was presented using multi-channel electromyogram data from the residual limb, and a model for muscle activity was built. Tuning using this model requires an exhaustive search over all the possible combinations of parameters, leading to computationally inefficient system. Various data-driven optimization methods were investigated and a modified Nelder–Mead algorithm using a Latin Hypercube Sampling method was introduced to tune the powered prosthetic. The results of the modified Nelder–Mead optimization were compared to the Exhaustive search, Genetic Algorithm, and conventional Nelder–Mead method, and the results showed the feasibility of using the presented method, to objectively calibrate the parameters in a time-efficient way using biological evidence.

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          Most cited references 56

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          Monte Carlo sampling methods using Markov chains and their applications

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                17 August 2018
                August 2018
                : 18
                : 8
                Affiliations
                [1 ]Human Cyber-Physical Systems Laboratory, Florida International University, Miami, FL 33174, USA; jmarq056@ 123456fiu.edu (J.S.M.); cleun006@ 123456fiu.edu (C.L.); msidd021@ 123456fiu.edu (M.R.S.); obai@ 123456fiu.edu (O.B.)
                [2 ]Department of Veterans Affairs, Hunter Holmes McGuire VA Medical Center, Richmond, VA 23249, USA; douglas.murphy3@ 123456va.gov (D.P.M.); ashraf.gorgey@ 123456va.gov (A.S.G.); william.lovegreen@ 123456va.gov (W.T.L.)
                [3 ]Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23220, USA; fei@ 123456vcu.edu
                Author notes
                [* ]Correspondence: ratri001@ 123456fiu.edu ; Tel.: +1-305-504-1460
                Article
                sensors-18-02705
                10.3390/s18082705
                6111278
                30126112
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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