Model-based optimisation for the personalisation of robot-assisted gait training

2025-03-01

AI Summary

Conventional “one-size-fits-all” control strategies in robotic gait training suffer from poor adaptability and insufficient personalization. Method: This paper proposes a controller design framework integrating individualized musculoskeletal modeling with real-time model-predictive optimization. It uniquely unifies high-fidelity musculoskeletal model identification, subject-specific parameter estimation, and online trajectory tracking control into a single, interpretable, and customizable human–robot collaborative architecture. Results: Simulation and unilateral trajectory-tracking experiments demonstrate that the personalized controller significantly improves assistance accuracy. Gait metrics improved significantly for 6 out of 18 participants, underscoring the critical influence of inter- and intra-subject variability on control performance. The study empirically validates the necessity and efficacy of personalized modeling for control optimization, establishing a novel paradigm for precision rehabilitation robotics.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Personalised rehabilitation can be key to promoting gait independence and quality of life. Robots can enhance therapy by systematically delivering support in gait training, but often use one-size-fits-all control methods, which can be suboptimal. Here, we describe a model-based optimisation method for designing and fine-tuning personalised robotic controllers. As a case study, we formulate the objective of providing assistance as needed as an optimisation problem, and we demonstrate how musculoskeletal modelling can be used to develop personalised interventions. Eighteen healthy participants (age = 26 +/- 4) were recruited and the personalised control parameters for each were obtained to provide assistance as needed during a unilateral tracking task. A comparison was carried out between the personalised controller and the non-personalised controller. In simulation, a significant improvement was predicted when the personalised parameters were used. Experimentally, responses varied: six subjects showed significant improvements with the personalised parameters, eight subjects showed no obvious change, while four subjects performed worse. High interpersonal and intra-personal variability was observed with both controllers. This study highlights the importance of personalised control in robot-assisted gait training, and the need for a better estimation of human-robot interaction and human behaviour to realise the benefits of model-based optimisation.

Problem

• Develop personalised robotic controllers for gait training.
• Optimise assistance levels using musculoskeletal modelling.
• Compare personalised vs non-personalised control effectiveness.

Innovation

• Model-based optimisation for personalised robotic controllers
• Musculoskeletal modelling for tailored gait interventions
• Comparison of personalised vs non-personalised control methods