A Prosthetic Shank With Adaptable Torsion Stiffness and Foot Alignment

Overview

Journal Front Neurorobot

Date 2020 May 28

PMID 32457590

Citations 1

Authors

Jochen Schuy

Nadine Stech

Graham Harris

Philipp Beckerle

Saeed Zahedi

Stephan Rinderknecht

Affiliations

Soon will be listed here.

Abstract

Torsion adapters in lower limb prostheses aim to increase comfort, mobility and health of users by allowing rotation in the transversal plane. A preliminary study with two transtibial amputees indicated correlations between torsional stiffness and foot alignment to increase comfort and stability of the user depending on the gait situation and velocity. This paper presents the design and proof-of-concept of an active, bio-inspired prosthetic shank adapter and a novel approach to create a user-specific human-machine interaction through adapting the device's properties. To provide adequate support, load data and subjective feedback of subjects are recorded and analyzed regarding defined gait situations. The results are merged to an user individual preference-setting matrix to select optimal parameters for each gait situation and velocity. A control strategy is implemented to render the specified desired torsional stiffness and transversal foot alignment values to achieve situation-dependent adaptation based on the input of designed gait detection algorithms. The proposed parallel elastic drive train mimics the functions of bones and muscles in the human shank. It is designed to provide the desired physical human-machine interaction properties along with optimized actuator energy consumption. Following test bench verification, trials with five participants with lower limb amputation at different levels are performed for basic validation. The results suggest improved movement support in turning maneuvers. Subjective user feedback confirmed a noticeable reduction of load at the stump and improved ease of turning.

Citing Articles

An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based 'Gold Standard'.

Zahradka N, Verma K, Behboodi A, Bodt B, Wright H, Lee S Sensors (Basel). 2020; 20(18).

PMID: 32942645 PMC: 7571134. DOI: 10.3390/s20185272.

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