Biomechanical assessment of adapting trajectory and human-robot interaction stiffness in impedance-controlled ankle orthosis

• Autor(a) principal: Lopes, João Pedro Mendes
• Data de Publicação: 2021
• Outros Autores: Figueiredo, Joana, Pinheiro, Cristiana Filipa Sampaio, Reis, Luís P., Santos, Cristina
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/84270
• topic: Adaptive assistive strategies; Impedance control; Human-orthosis interaction; Locomotion and actuation systems; Robotic rehabilitation; Engenharia e Tecnologia::Engenharia Médica; Science & Technology; Saúde de qualidade

Gait disabilities empowered intensive research on the field of human-robot interaction to promote effective gait rehabilitation. Assist-as-needed strategies are becoming prominent, appealing to the users’ participation in their rehabilitation therapy. This study proposes and assesses the biomechanical effects of an adaptive impedance control strategy that innovatively allows adaptability in interaction-based stiffness and gait trajectory towards a fully assist-as-needed therapy. By modulating the interaction-based stiffness per gait phase, we hypothesize that the strategy appeals to a symbiotic human-orthotic cooperation, augmenting the user’s muscular activity. The interaction stiffness was estimated by modelling the human-orthosis interaction torque vs angle curve with a linear regression model. The strategy also allows for real-time trajectory adaptations at different gait phases to fulfil the users’ needs. The biomechanical assessment of the impedance-controlled ankle orthosis involved eight healthy volunteers walking at 1.0 and 1.6 km/h. The results revealed a stronger muscular activation regarding the non-assisted leg for the gastrocnemius lateralis (increment ratio ≥ 1.0 for both gait speeds) and for the tibialis anterior muscle (increment ratio ≥ 1.0 for 1.6 km/h). The strategy guided users successfully on a healthy gait pattern while allowing deviations (median error < 5.0°) given the users’ intention weighted by interaction stiffness. Findings showed the relevance for adapting gait trajectory as users prefer higher trajectories as the speed increases. No significant temporal variations or neither knee angular compensations were observed (p value ≥0.11). Overall results support that this strategy may be applied for intensity-adapted gait training, allowing different human-robot compliant levels.