Benjamin Filtjens Human Movement Intelligence

Research project

Towards at-home mobility assessment

Developing movement-assessment methods and collaborations for research beyond the laboratory.

Completed Fellowship Movement in clinical practiceLearning from human movement
Role
Personal fellowship holder
Period
2022-10–2023-09
Funders
KU Leuven Research Council · PDMT2/22/046
Illustrated gait assessment with wearable sensors, seated exercise with a robotic assistant, and lower-limb exoskeleton rehabilitation, connected through shared research methods and collaborations.

A conceptual research landscape: movement-assessment methods and collaborations connecting gait assessment, exercise support and assistive rehabilitation.

Movement assessment outside the laboratory requires methods that can handle less controlled recordings and varied activities. The KU Leuven PDM fellowship supported AI-based movement assessment in people with Parkinson’s disease and older care-home residents. Work included extending gait analysis towards wearable sensors, developing collaborations for multicentre validation, and piloting movement assessment in care homes.

The fellowship also supported collaborative proposal development, contributing to follow-on research in exercise support, home monitoring and assistive rehabilitation. AI@WZC and RevalExo developed these directions further, while proposals for AID-FOG and AidWear were developed during this period.

Related work

Publications

2026

Journal Article

Deep learning for freezing of gait assessment using inertial measurement units: a multicentre validation study

Yang PK, Carlon J, Goris M, Klaver E, Nonnekes J, van Wezel RJA, Alcock L, Yarnall AJ, Rochester L, Hansen C, Schlenstedt C, Maetzler W, Buzaglo D, Brozgol M, Hausdorff JM, Nieuwboer A, Gilat M, Ginis P, Vanrumste B, Filtjens B

npj Parkinson's Disease, 2026

Citation

Yang PK, Carlon J, Goris M, Klaver E, Nonnekes J, van Wezel RJA, Alcock L, Yarnall AJ, Rochester L, Hansen C, Schlenstedt C, Maetzler W, Buzaglo D, Brozgol M, Hausdorff JM, Nieuwboer A, Gilat M, Ginis P, Vanrumste B, Filtjens B (2026). Deep learning for freezing of gait assessment using inertial measurement units: a multicentre validation study. npj Parkinson's Disease, 2026. https://doi.org/10.1038/s41531-026-01407-7

2025

Journal Article

Technology for measuring freezing of gait: Current state of the art and recommendations

Mancini M, McKay JL, Cockx H, D’Cruz N, Esper CD, Filtjens B, Heimler B, MacKinnon CD, Palmerini L, Roerdink M, Young WR, Hausdorff JM, ICFOG Investigators

Journal of Parkinson’s Disease 15 (1), 19-40, 2025

Citation

Mancini M, McKay JL, Cockx H, D’Cruz N, Esper CD, Filtjens B, Heimler B, MacKinnon CD, Palmerini L, Roerdink M, Young WR, Hausdorff JM, ICFOG Investigators (2025). Technology for measuring freezing of gait: Current state of the art and recommendations. Journal of Parkinson’s Disease 15 (1), 19-40, 2025. https://doi.org/10.1177/1877718X241301065

2022

Journal Article

Skeleton-based action segmentation with multi-stage spatial-temporal graph convolutional neural networks

Filtjens B, Vanrumste B, Slaets P

IEEE Transactions on Emerging Topics in Computing 12 (1), 202-212, 2022

Citation

Filtjens B, Vanrumste B, Slaets P (2022). Skeleton-based action segmentation with multi-stage spatial-temporal graph convolutional neural networks. IEEE Transactions on Emerging Topics in Computing 12 (1), 202-212, 2022. https://doi.org/10.1109/TETC.2022.3230912