An adapted Gaussian mixture model approach to accelerometry-based movement classification using time-domain features.
The accurate classification of everyday movements from accelerometry data will provide a significant step towards the development of effective ambulatory monitoring systems for falls detection and prediction. The search continues for optimal front-end processing methods for use in accelerometry systems. Here, we propose a novel set of time domain features, which achieve a mean accuracy of 91.3% in distinguishing between three postures (sitting, standing and lying) and five movements (sit-to-stand, stand-to-sit, lie-to-stand, stand-to-lie and walking). This is a 39.2% relative improvement in error rate over more commonly used frequency based features. A method for adapting Gaussian Mixture Models to compensate for the problem of limited user-specific training data is also proposed and investigated. The method, which uses Bayesian adaptation, was found to improve classification performance for time domain features, offering a mean relative improvement of 20.2% over a non subject-specific system and 4.5% over a system trained using subject specific data only.
Allen
Felicity R
FR
Graduate Sch. of Biomed. Eng., New South Wales Univ., Sydney, NSW.
Ambikairajah
Eliathamby
E
Lovell
Nigel H
NH
Celler
Branko G
BG
D000054:Acceleration; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D005684:Gait; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory; D009043:Motor Activity; D009068:Movement / Q000502:physiology*; D016011:Normal Distribution; D011187:Posture; D016138:Walking / Q000502:physiology*; D016474:Weight-Bearing