Enhancing gait rehabilitation using robotic assistance and functional electrical stimulation

Abstract

Wearable robots and assistive exoskeletons have great potential as tools for rehabilitation and assisted living. By providing support to dedicated joints and body segments, these devices can foster the independence of people suffering from neurological diseases and improve quality of life. However, ensuring these devices respond appropriately to the unique needs of each patient is crucial, as it can play a decisive role in whether neural plasticity is induced. This is particularly challenging as patients suffering from stroke or incomplete spinal cord injury start regaining control of their limbs, where rigid robotdriven interventions are no longer adequate to facilitate recovery and more adaptive patient-driven interventions are required.

Motivated by the principles of neuroplasticity, this thesis delves into the integration of wearable robots in neurological gait rehabilitation, with a central focus on the design of personalised interventions for ambulatory patients. More specifically, this thesis explores methods for the optimisation of robotic controllers and collaborative functional electrical stimulation (FES) controllers, such that assistance can be provided as needed, encouraging the patient to use their residual strength and actively take part in gait training.

Firstly, we formulate the problem of providing assistance ‘as needed’ as an optimisation problem and propose an offline model-based optimisation method for the design of personalised rehabilitation interventions. Using motion capture and high-fidelity musculoskeletal models, we construct a personalised model of the human interacting with the robot, and we optimise the controller of the robot using forward dynamics. We describe how this method can be applied for both the design of novel near-optimal surrogate controllers in the real-world, as well as the fine-tuning of parameterised controllers with a known control structure. The effect of the offline model-based optimisation method is evaluated both in simulation and experimentally, highlighting the need for personalisation and the importance of capturing the inter-personal and intra-personal variability in human behaviour.

An alternative to model-based optimisation is human-in-the-loop optimisation, where the human response to different levels of assistance can be obtained in real time, reducing uncertainties due to modelling bias. Human-in-the-loop optimisation has proven to be an effective method for reducing the metabolic cost in robot-assisted locomotion, but its potential in enhancing rehabilitation has not been explored. We hypothesise that with the use of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), time-dependent variations in gait may be captured, facilitating the detection of time-varying local minima. Using continuous optimisation over a two-day experimental protocol we carry out a preliminary study of human-in-the-loop optimisation and present the results obtained from healthy subjects.

Beyond the deployment of robotics in neural rehabilitation, numerous physiological benefits can be achieved with the use of functional electrical stimulation (FES). Particularly interesting is the integration of robotics with FES, as the two have several complementary characteristics. However, due to the increased complexity of hybrid robot-FES systems, controller personalisation through model-based or human-in-the-loop optimisation becomes increasingly demanding. To promote the triadic collaboration between human, robot and FES, we propose a novel hierarchical and adaptive controller. We demonstrate a hybrid system that can prioritise the voluntary contributions of the human and effectively distribute the necessary assistive forces between the robot and the FES, in order to delay the onset of muscle fatigue and provide assistance as needed.

These methods contribute towards the advancement of techniques for designing personalised interventions for gait rehabilitation, which could lead to improved functional outcomes and accelerated recovery after training.