Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life

Autor: Max A. Little, Yordan P. Raykov, Marjan J. Meinders, Reham Badawy, Luc J.W. Evers, Tom Heskes, Bastiaan R. Bloem, Kasper Claes
Rok vydání: 2020
Předmět:
Signal Processing (eess.SP)
FOS: Computer and information sciences
Activities of daily living
Computer science
Feature extraction
Computer Science - Human-Computer Interaction
Wearable computer
Walking
Machine learning
computer.software_genre
Accelerometer
01 natural sciences
Human-Computer Interaction (cs.HC)
Healthcare improvement science Radboud Institute for Health Sciences [Radboudumc 18]
03 medical and health sciences
Wearable Electronic Devices
0302 clinical medicine
Gait (human)
All institutes and research themes of the Radboud University Medical Center
Health Information Management
Activities of Daily Living
FOS: Electrical engineering
electronic engineering
information engineering
Humans
Electrical Engineering and Systems Science - Signal Processing
Electrical and Electronic Engineering
Hidden Markov model
Gait
business.industry
010401 analytical chemistry
Work (physics)
Data Science
Passive monitoring
Parkinson Disease
Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3]
0104 chemical sciences
Computer Science Applications
Artificial intelligence
business
computer
human activities
030217 neurology & neurosurgery
Biotechnology
Zdroj: IEEE Journal of Biomedical and Health Informatics, 25, 6, pp. 2293-2304
IEEE Journal of Biomedical and Health Informatics, 25, 2293-2304
ISSN: 2168-2208
2168-2194
Popis: Passive monitoring in daily life may provide invaluable insights about a person's health throughout the day. Wearable sensor devices are likely to play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflects multiple health and behavior related factors together. This creates the need for structured principled analysis to produce reliable and interpretable predictions that can be used to support clinical diagnosis and treatment. In this work we develop a principled modelling approach for free-living gait (walking) analysis. Gait is a promising target for non-obtrusive monitoring because it is common and indicative of various movement disorders such as Parkinson's disease (PD), yet its analysis has largely been limited to experimentally controlled lab settings. To locate and characterize stationary gait segments in free living using accelerometers, we present an unsupervised statistical framework designed to segment signals into differing gait and non-gait patterns. Our flexible probabilistic framework combines empirical assumptions about gait into a principled graphical model with all of its merits. We demonstrate the approach on a new video-referenced dataset including unscripted daily living activities of 25 PD patients and 25 controls, in and around their own houses. We evaluate our ability to detect gait and predict medication induced fluctuations in PD patients based on modelled gait. Our evaluation includes a comparison between sensors attached at multiple body locations including wrist, ankle, trouser pocket and lower back.
Databáze: OpenAIRE
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