Biomechanical knee energy harvester: Design optimization and testing

Moran Gad, Ben Lev-Ari, Amir Shapiro, Coral Ben-David, Raziel Riemer

Research output: Contribution to journalArticlepeer-review

Abstract

Biomechanical energy harvesters are designed to generate electrical energy from human locomotion (e.g., walking) with minimal or no additional effort by the users. These harvesters aim to carry out the work of the muscles during phases in locomotion where the muscles are acting as brakes. Currently, many harvesters focus on the knee joint during late swing, which is only one of three phases available during the gait cycle. For the device to be successful, there is a need to consider design components such as the motor/generator and the gear ratio. These components influence the amount of electrical energy that could be harvested, metabolic power during harvesting, and more. These various components make it challenging to achieve the optimal design. This paper presents a design of a knee harvester with a direct drive that enables harvesting both in flexion and extension using optimization. Subsequently, two knee devices were built and tested using five different harvesting levels. Results show that the 30% level was the best, harvesting approximately 5 W of electricity and redacting 8 W of metabolic energy compared to walking with the device as a dead weight. Evaluation of the models used in the optimization showed a good match to the system model but less for the metabolic power model. These results could pave the way for an energy harvester that could utilize more of the negative joint power during the gait cycle while reducing metabolic effort.

Original languageEnglish
Article number998248
JournalFrontiers in Robotics and AI
Volume9
DOIs
StatePublished - 5 Oct 2022

Keywords

  • biomechanical knee energy harvester
  • design optimization
  • exoskeleton
  • reduced effort
  • simulation

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

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