Leg Force Control Through Biarticular Muscles for Human Walking Assistance

Overview

Journal Front Neurorobot

Date 2018 Jul 28

PMID 30050426

Citations 9

Authors

Maziar A Sharbafi

Hamid Barazesh

Majid Iranikhah

Andre Seyfarth

Affiliations

Soon will be listed here.

Abstract

Assistive devices can be considered as one of the main applications of legged locomotion research in daily life. In order to develop an efficient and comfortable prosthesis or exoskeleton, biomechanical studies on human locomotion are very useful. In this paper, the applicability of the FMCH (force modulated compliant hip) model is investigated for control of lower limb wearable exoskeletons. This is a bioinspired method for posture control, which is based on the virtual pivot point (VPP) concept, found in human walking. By implementing the proposed method on a detailed neuromuscular model of human walking, we showed that using a biarticular actuator parallel to the hamstring muscle, activation in most of the leg muscles can be reduced. In addition, the total metabolic cost of motion is decreased up to 12%. The simple control rule of assistance is based on leg force feedback which is the only required sensory information.

Citing Articles

Biomechanical models in the lower-limb exoskeletons development: a review.

Firouzi V, Seyfarth A, Song S, von Stryk O, Ahmad Sharbafi M J Neuroeng Rehabil. 2025; 22(1):12.

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Comparison of Empirical and Reinforcement Learning (RL)-Based Control Based on Proximal Policy Optimization (PPO) for Walking Assistance: Does AI Always Win?.

Drewing N, Ahmadi A, Xiong X, Ahmad Sharbafi M Biomimetics (Basel). 2024; 9(11).

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Evaluation of controllers for augmentative hip exoskeletons and their effects on metabolic cost of walking: explicit versus implicit synchronization.

Manzoori A, Malatesta D, Primavesi J, Ijspeert A, Bouri M Front Bioeng Biotechnol. 2024; 12:1324587.

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Towards Human-like Walking with Biomechanical and Neuromuscular Control Features: Personalized Attachment Point Optimization Method of Cable-Driven Exoskeleton.

Chen Y, Yu W, Benali A, Lu D, Kok S, Wang R Front Aging Neurosci. 2024; 16:1327397.

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Simulating human walking: a model-based reinforcement learning approach with musculoskeletal modeling.

Su B, Gutierrez-Farewik E Front Neurorobot. 2023; 17:1244417.

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