Assist-As-Needed Control of Soft Ankle Orthosis for Walking in Patients with Foot Drop

This article investigates a soft robotic ankle-foot orthosis that aims to enhance gait rehabilitation and reduce foot drop. The orthosis is lightweight, entirely untethered, and operated by a bi-directional tendon-driven actuator that facilitates both dorsiflexion and plantar flexion. It incorporates an adaptive Assist-As-Needed (AAN) controller that customizes assistance based on user capabilities through a need index, which integrates interaction force and positional tracking errors. The orthosis utilizes soft materials and a cable-driven mechanism to enhance flexibility and comfort for the user, allowing free ankle movement while maintaining structural integrity. Real-time gait sensing, implemented through a smart insole with force-sensitive resistors, enables accurate control in accordance with the user's biomechanics. Experimental results demonstrate that the orthosis maintained the ankle within the desired gait trajectory for almost 93 % of the gait cycle, with improvements in gait propulsion and foot-drop prevention. Additionally, the time of each gait phase closely matched the desired one, with the swing phase lasting 0.158 seconds longer than expected. This technology, which integrates a highly responsive gait detection system and a cable-driven robotic mechanism, is a viable method for rehabilitation with minimal intervention.

Authors

Institutions

Publication Details

Published
2024-12-17
DOI
https://doi.org/10.1109/icrom64545.2024.10903538
Primary Topic
Foot and Ankle Surgery
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assist-As-Needed Control of Soft Ankle Orthosis for Walking in Patients with Foot Drop

Mohammad Zareinejad, Heidar Ali Talebi, Pouria Sheikhi
Foot and Ankle Surgery
article

Assist-As-Needed Control of Soft Ankle Orthosis for Walking in Patients with Foot Drop

Mohammad Zareinejad, Heidar Ali Talebi, Pouria Sheikhi
article en

Abstract

This article investigates a soft robotic ankle-foot orthosis that aims to enhance gait rehabilitation and reduce foot drop. The orthosis is lightweight, entirely untethered, and operated by a bi-directional tendon-driven actuator that facilitates both dorsiflexion and plantar flexion. It incorporates an adaptive Assist-As-Needed (AAN) controller that customizes assistance based on user capabilities through a need index, which integrates interaction force and positional tracking errors. The orthosis utilizes soft materials and a cable-driven mechanism to enhance flexibility and comfort for the user, allowing free ankle movement while maintaining structural integrity. Real-time gait sensing, implemented through a smart insole with force-sensitive resistors, enables accurate control in accordance with the user's biomechanics. Experimental results demonstrate that the orthosis maintained the ankle within the desired gait trajectory for almost 93 % of the gait cycle, with improvements in gait propulsion and foot-drop prevention. Additionally, the time of each gait phase closely matched the desired one, with the swing phase lasting 0.158 seconds longer than expected. This technology, which integrates a highly responsive gait detection system and a cable-driven robotic mechanism, is a viable method for rehabilitation with minimal intervention.

Amirkabir University of Technology (IR)
Openalex Percentile: Top 64%
Foot and Ankle Surgery
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Assist-As-Needed Control of Soft Ankle Orthosis for Walking in Patients with Foot Drop — Mohammad Zareinejad, Heidar Ali Talebi, et al. · (2024) | TGRS Research Map | TGRS