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Article

Personalised Gait Recognition for People with Neurological Conditions

1
LASIGE, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal
2
Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, Skopje 1000, North Macedonia
3
Instituto de Medicina Molecular João Lobo Antunes, 1649-028 Lisbon, Portugal
4
CNS—Campus Neurológico, 2560-280 Torres Vedras, Portugal
5
Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
List of collaborators of the CNS Physiotherapy Study Group is provided in the Acknowledgments.
Academic Editors: Rosie Morris and Silvia Del Din
Sensors 2022, 22(11), 3980; https://doi.org/10.3390/s22113980
Received: 31 March 2022 / Revised: 12 May 2022 / Accepted: 18 May 2022 / Published: 24 May 2022
(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)
There is growing interest in monitoring gait patterns in people with neurological conditions. The democratisation of wearable inertial sensors has enabled the study of gait in free living environments. One pivotal aspect of gait assessment in uncontrolled environments is the ability to accurately recognise gait instances. Previous work has focused on wavelet transform methods or general machine learning models to detect gait; the former assume a comparable gait pattern between people and the latter assume training datasets that represent a diverse population. In this paper, we argue that these approaches are unsuitable for people with severe motor impairments and their distinct gait patterns, and make the case for a lightweight personalised alternative. We propose an approach that builds on top of a general model, fine-tuning it with personalised data. A comparative proof-of-concept evaluation with general machine learning (NN and CNN) approaches and personalised counterparts showed that the latter improved the overall accuracy in 3.5% for the NN and 5.3% for the CNN. More importantly, participants that were ill-represented by the general model (the most extreme cases) had the recognition of gait instances improved by up to 16.9% for NN and 20.5% for CNN with the personalised approaches. It is common to say that people with neurological conditions, such as Parkinson’s disease, present very individual motor patterns, and that in a sense they are all outliers; we expect that our results will motivate researchers to explore alternative approaches that value personalisation rather than harvesting datasets that are may be able to represent these differences. View Full-Text
Keywords: gait recognition; accelerometers; neurological conditions; motor impairments; personalisation; neural networks gait recognition; accelerometers; neurological conditions; motor impairments; personalisation; neural networks
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MDPI and ACS Style

Ingelse, L.; Branco, D.; Gjoreski, H.; Guerreiro, T.; Bouça-Machado, R.; Ferreira, J.J.; The CNS Physiotherapy Study Group. Personalised Gait Recognition for People with Neurological Conditions. Sensors 2022, 22, 3980. https://doi.org/10.3390/s22113980

AMA Style

Ingelse L, Branco D, Gjoreski H, Guerreiro T, Bouça-Machado R, Ferreira JJ, The CNS Physiotherapy Study Group. Personalised Gait Recognition for People with Neurological Conditions. Sensors. 2022; 22(11):3980. https://doi.org/10.3390/s22113980

Chicago/Turabian Style

Ingelse, Leon, Diogo Branco, Hristijan Gjoreski, Tiago Guerreiro, Raquel Bouça-Machado, Joaquim J. Ferreira, and The CNS Physiotherapy Study Group. 2022. "Personalised Gait Recognition for People with Neurological Conditions" Sensors 22, no. 11: 3980. https://doi.org/10.3390/s22113980

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