IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025
Reducing Muscular Effort in Exoskeletons: A Bioinspired Design and Hierarchical Motion Recognition Framework.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Yan Lingyun, Xiu Haohua, Wei Yuyang, Cao Kaitian, Luo Xudong, Li Yiqi
Аннотация
UNLABELLED: This study aims to address the limitations of traditional exoskeleton designs by developing a biomimetic actuation path and a hierarchical motion recognition framework to improve integration with human biomechanics and reduce muscular effort during walking.
METHODS: A musculoskeletal model was used to quantify lower limb muscle force patterns, enabling the design of actuation paths aligned with natural muscle contraction trajectories. A hierarchical motion recognition system, combining an auto-encoder and an artificial neural network (ANN), was developed for real-time identification of gait events, activity levels, and walking speeds. Two biomechanical-inspired control strategies were implemented to replicate natural movement patterns and adapt to dynamic forces during walking.
RESULTS: Experimental validation through EMG-based walking trials demonstrated a significant reduction in muscle activity. Specifically, the exoskeleton reduced the maximum voluntary isometric contraction (%MVIC) of the soleus muscle by 12.39% and the gastrocnemius by 12.32% compared to unassisted walking.
CONCLUSION: The proposed design effectively integrates human-exoskeleton interaction, reduces muscular effort, and provides precise motion assistance, offering a novel approach to incorporating muscle force analysis in wearable robotics.
SIGNIFICANCE: This work advances the field of exoskeleton technology by introducing a quantitative biomechanical approach for actuation path optimization and real-time motion recognition, with potential applications in rehabilitation, assistive devices, and human locomotion enhancement.
METHODS: A musculoskeletal model was used to quantify lower limb muscle force patterns, enabling the design of actuation paths aligned with natural muscle contraction trajectories. A hierarchical motion recognition system, combining an auto-encoder and an artificial neural network (ANN), was developed for real-time identification of gait events, activity levels, and walking speeds. Two biomechanical-inspired control strategies were implemented to replicate natural movement patterns and adapt to dynamic forces during walking.
RESULTS: Experimental validation through EMG-based walking trials demonstrated a significant reduction in muscle activity. Specifically, the exoskeleton reduced the maximum voluntary isometric contraction (%MVIC) of the soleus muscle by 12.39% and the gastrocnemius by 12.32% compared to unassisted walking.
CONCLUSION: The proposed design effectively integrates human-exoskeleton interaction, reduces muscular effort, and provides precise motion assistance, offering a novel approach to incorporating muscle force analysis in wearable robotics.
SIGNIFICANCE: This work advances the field of exoskeleton technology by introducing a quantitative biomechanical approach for actuation path optimization and real-time motion recognition, with potential applications in rehabilitation, assistive devices, and human locomotion enhancement.