Lucas Fonseca
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RESEARCH

Understanding movement.
Designing useful assistance.

Human movement is the core of my research. It helps us understand ability, recognise intention and evaluate whether a technology actually helps.

A movement-centred approach

My work begins with the person: their available movement, the task they want to perform and the context in which they act. I bring together wearable sensing, movement analysis, machine learning and control to develop systems that work for and with the user.

01

Movement and intention

Small movements and muscle contractions can be useful sources of information, even when conventional controls are inaccessible. I investigate how these signals can become reliable, personalised interfaces for assistive devices.

Methods

Inertial measurement units (IMUs), electromyography (EMG), signal processing, movement classification and individual calibration.

Explore residual-movement interfaces

02

Rehabilitation and assistive technology

Movement should inform both the assistance a system provides and how that assistance is evaluated. My earlier work includes functional electrical stimulation for cycling, transfers, rowing and grasp assistance after spinal cord injury.

Methods

Movement-based control, functional electrical stimulation, instrumentation, system integration and experimental evaluation.

Explore stimulation and rehabilitation

03

Human–robot interaction and intelligent mobility

Assistive and collaborative robots need to interpret what a person is trying to do, and respond in a way that preserves their agency. Current work applies this perspective to predictive smart-walker support and accessible shared control for early-years powered mobility.

Methods

Sensor fusion, trajectory prediction, intention interfaces, shared control and user-centred design.

Smart-walker collision risk · SMILE powered mobility

04

Movement assessment and motor learning

Sensing can help describe physical function, but a measurable signal is not automatically a meaningful clinical measure. This theme connects movement and physiological measurement with rehabilitation questions, including sensor-derived biomarkers and the role of human motor learning in rehabilitation AI.

Research questions

Which measures reflect function? How well do they generalise? How can assistance support learning, rather than only immediate task performance?

Related publications and reading

Working together

I welcome complementary expertise in movement science, rehabilitation, clinical practice, interaction design, psychology and robotics.

Discuss a research connection

Lucas Fonseca
Human movement for rehabilitation, health and independence.

 

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University of Nottingham