A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors.

Autor: Wairagkar M; Department of Mechanical Engineering, Imperial College London, London, United Kingdom.; Care Research and Technology Centre, UK Dementia Research Institute, London, United Kingdom.; Department of Biomedical Engineering, University of Reading, Reading, United Kingdom., Villeneuve E; Univ. Grenoble Alpes, CEA, LETI, DTBS, LS2P, Grenoble, France., King R; Department of Biomedical Engineering, University of Reading, Reading, United Kingdom., Janko B; Department of Biomedical Engineering, University of Reading, Reading, United Kingdom., Burnett M; School of Health Sciences, University of Southampton, Southampton, United Kingdom., Agarwal V; School of Health Sciences, University of Southampton, Southampton, United Kingdom., Kunkel D; School of Health Sciences, University of Southampton, Southampton, United Kingdom., Ashburn A; School of Health Sciences, University of Southampton, Southampton, United Kingdom., Sherratt RS; Department of Biomedical Engineering, University of Reading, Reading, United Kingdom., Holderbaum W; Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, United Kingdom., Harwin WS; Department of Biomedical Engineering, University of Reading, Reading, United Kingdom.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2022 Oct 18; Vol. 17 (10), pp. e0264126. Date of Electronic Publication: 2022 Oct 18 (Print Publication: 2022).
DOI: 10.1371/journal.pone.0264126
Abstrakt: Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson's disease leading to falls. Studying kinematics of sit-to-stand transitions can provide insight in assessment, monitoring and developing rehabilitation strategies for the affected populations. We propose a three-segment body model for estimating sit-to-stand kinematics using only two wearable inertial sensors, placed on the shank and back. Reducing the number of sensors to two instead of one per body segment facilitates monitoring and classifying movements over extended periods, making it more comfortable to wear while reducing the power requirements of sensors. We applied this model on 10 younger healthy adults (YH), 12 older healthy adults (OH) and 12 people with Parkinson's disease (PwP). We have achieved this by incorporating unique sit-to-stand classification technique using unsupervised learning in the model based reconstruction of angular kinematics using extended Kalman filter. Our proposed model showed that it was possible to successfully estimate thigh kinematics despite not measuring the thigh motion with inertial sensor. We classified sit-to-stand transitions, sitting and standing states with the accuracies of 98.67%, 94.20% and 91.41% for YH, OH and PwP respectively. We have proposed a novel integrated approach of modelling and classification for estimating the body kinematics during sit-to-stand motion and successfully applied it on YH, OH and PwP groups.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje