On 30 October 2026 at 16:00, Silvia Zaccardi will defend their PhD entitled “Beyond the lab: Towards portable clinical motion analysis with observer-worn head-mounted displays”.

Everybody is invited to attend the presentation in room LIC.0.04 or online via this link.

Human movement analysis provides valuable information for assessing patients and monitoring rehabilitation. However, accurate measurements typically require expensive equipment in specialised laboratories, limiting their use in everyday clinical practice.

This thesis explores how Artificial Intelligence (AI) and Mixed Reality (MR) headsets could make movement analysis more affordable, portable, and accessible. The long-term vision is a system in which a physiotherapist wears an MR headset while a patient walks or performs exercises in front of them. Cameras built into the headset would track the patient’s movements, while relevant information, such as joint angles and walking characteristics, would be displayed directly in the physiotherapist’s field of view, supporting clinical assessment in real time.

Towards this vision, the research proposes two approaches to capturing human movement using the headset’s built-in cameras and sensors. The first uses AI to estimate body movement directly from video, without requiring physical markers, while the second uses small reflective markers attached to the patient’s body to improve tracking accuracy. The thesis also explores AI algorithms to estimate the forces between the feet and the ground from movement data alone, potentially reducing the need for specialised force-measuring plates embedded in the laboratory floor.

The findings demonstrate the potential of these technologies while highlighting the trade-offs between measurement accuracy, ease of use, and real-time performance. Overall, this thesis contributes towards making objective movement analysis more accessible beyond specialised laboratories.