A Numerical Feasibility Study of Kinetic Energy Harvesting from Lower Limb Prosthetics

Abstract

With the advancement trend of lower limb prosthetics headed towards bionics (active ankle and knee) and smart prosthetics (gait and condition monitoring), there is an increasing integration of various sensors (micro-electromechanical system (MEMS) accelerometers, gyroscopes, magnetometers, strain gauges, pressure sensors, etc.), microcontrollers and wireless systems, and power drives including motors and actuators. All of these active elements require electrical power. However, inclusion of a heavy and bulky battery risks to undo the lightweight advancements achieved by the strong and flexible composite materials in the past decades. Kinetic energy harvesting holds the promise to recharge a small on-board battery in order to sustain the active systems without sacrificing weight and size. However, careful design is required in order not to over-burden the user from parasitic effects. This paper presents a feasibility study using measured gait data and numerical simulation in order to predict the available recoverable power. The numerical simulations suggest that, depending on the axis, up to 10s mW average electrical power is recoverable for a walking gait and up to 100s mW average electrical power is achievable during a running gait. This takes into account parasitic losses and only capturing a fraction of the gait cycle to not adversely burden the user. The predicted recoverable power levels are ample to self-sustain wireless communication and smart sensing functionalities to support smart prosthetics, as well as extend the battery life for active actuators in bionic systems. The results here serve as a theoretical foundation to design and develop towards regenerative smart bionic prosthetics.

Publication DOI: https://doi.org/10.3390/en12203824
Divisions: College of Engineering & Physical Sciences
Additional Information: © 2019 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/).
Publication ISSN: 1996-1073
Full Text Link:
Related URLs: https://www.mdp ... 12/20/3824/htm# (Publisher URL)
PURE Output Type: Article
Published Date: 2019-10-10
Accepted Date: 2019-10-06
Authors: Jia, Yu (ORCID Profile 0000-0001-9640-1666)
Wei, Xueyong
Pu, Jie
Xie, Pengheng
Wen, Tao
Wang, Congsi
Lian, Peiyuan
Xue, Song
Shi, Yu

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