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Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

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Biomechanical knee energy harvester: Design optimization and testing.

Moran Gad¹, Ben Lev-Ari¹, Amir Shapiro¹

¹Mechanical Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

Frontiers in Robotics and AI

|October 24, 2022

Summary

This study optimized a knee-based biomechanical energy harvester. The best design harvested 5W of electricity and reduced metabolic energy by 8W during walking.

Keywords:

biomechanical knee energy harvester design optimization exoskeleton reduced effort simulation

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Area of Science:

Biomechanical Engineering
Wearable Technology
Energy Harvesting

Background:

Biomechanical energy harvesters aim to generate electricity from human locomotion with minimal user effort.
Current designs often focus on limited phases of the gait cycle, like the knee joint during late swing.
Optimizing components like motor/generator and gear ratio is crucial for effective energy harvesting and metabolic efficiency.

Purpose of the Study:

To present an optimized design for a direct-drive knee harvester capable of harvesting energy during both flexion and extension.
To evaluate the performance of the optimized harvester in terms of electrical energy generation and metabolic cost reduction.

Main Methods:

Development of an optimized direct-drive knee harvester design.
Construction and testing of two knee harvester prototypes.
Evaluation across five different harvesting levels to determine optimal performance.

Main Results:

The optimal harvesting level achieved approximately 5W of electrical energy generation.
A reduction of 8W in metabolic energy was observed compared to walking with the device as dead weight.
Optimization models showed good correlation with the system model but less so with the metabolic power model.

Conclusions:

The optimized direct-drive knee harvester effectively captures energy during both flexion and extension phases of the gait cycle.
This technology has the potential to reduce metabolic effort by utilizing negative joint power more efficiently.
Further refinement of metabolic power models is needed for enhanced predictive accuracy.