Researcher: Nicolas Kantor Nagel, Hossein Kaviani rad, Satoshi Endo

Motivation

Functional electrical stimulation (FES) can restore or assist movement in individuals with neurological impairments such as stroke, spinal cord injury, or neuromuscular disease by activating paralysed or weakened muscles. Beyond assisting movement execution, FES engages the neuromuscular system directly, providing strong sensory feedback to the central nervous system and promoting neuroplasticity.

The challenge lies in controlling FES effectively. The neuromuscular response to stimulation is non‑linear, noisy, and time‑varying, influenced by factors such as electrode placement, muscle fatigue, changing recruitment patterns, and skin–electrode interface conditions. Excessive stimulation can cause discomfort, fatigue, and reduced engagement, while insufficient stimulation fails to achieve the intended movement. Furthermore, voluntary muscle activity and stimulation‑evoked responses overlap in surface EMG signals, complicating both intent estimation and feedback control.

Research questions

• How can FES‑evoked muscle contractions be modelled in a way that captures non‑linear, time‑varying, and user‑specific response characteristics?
• How can voluntary intent be estimated from EMG even when stimulation artefacts and evoked potentials are present?
• How can stimulation intensity and timing be adapted in real time to maximise functional movement while avoiding over‑stimulation or fatigue?
• How can uncertainty in stimulation response be quantified and incorporated into safe, robust control strategies?

Approach

We are developing adaptive, EMG‑informed control frameworks for FES that combine:

• Robust EMG processing to separate voluntary signals from stimulation artefacts and evoked potentials in real time.
• Probabilistic, user‑specific models of muscle activation that capture the time‑varying nature of stimulation response and adapt continuously during use.
• Uncertainty‑aware control that adjusts stimulation parameters dynamically based on confidence in predicted outcomes, ensuring safety and reliability.
• Personalised calibration that uses short measurement sessions to initialise the model and refine parameters as the system interacts with the user.

By closing the loop between intent estimation, stimulation delivery, and real‑time monitoring of response, the system can provide precisely the right stimulation at the right time. In clinical populations such as stroke survivors, this allows FES to be delivered in synchrony with voluntary effort, reinforcing motor pathways and promoting recovery rather than replacing muscle activity.

Key results and achievements

• Developed robust EMG processing pipelines capable of operating during concurrent stimulation and voluntary movement.
• Designed adaptive stimulation controllers that account for fatigue and recruitment changes, maintaining stable movement quality over time.
• Demonstrated that EMG‑informed FES control improves synchronisation between voluntary intent and stimulation‑induced movement.
• Showed that personalised stimulation strategies can maintain comfort while achieving functional movement goals.