Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models.
Accelerometry shows promise in providing an inexpensive but effective means of long-term ambulatory monitoring of elderly patients. The accurate classification of everyday movements should allow such a monitoring system to exhibit greater 'intelligence', improving its ability to detect and predict falls by forming a more specific picture of the activities of a person and thereby allowing more accurate tracking of the health parameters associated with those activities. With this in mind, this study aims to develop more robust and effective methods for the classification of postures and motions from data obtained using a single, waist-mounted, triaxial accelerometer; in particular, aiming to improve the flexibility and generality of the monitoring system, making it better able to detect and identify short-duration movements and more adaptable to a specific person or device. Two movement classification methods were investigated: a rule-based Heuristic system and a Gaussian mixture model (GMM)-based system. A novel time-domain feature extraction method is proposed for the GMM system to allow better detection of short-duration movements. A method for adapting the GMMs to compensate for the problem of limited user-specific training data is also proposed and investigated. Classification performance was considered in relation to data gathered in an unsupervised, directed routine conducted in a three-month field trial involving six elderly subjects. The GMM system was found to achieve a mean accuracy of 91.3%, 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), compared to 71.1% achieved by the Heuristic system. The adaptation method was found to offer a mean accuracy of 92.2%; a relative improvement of 20.2% over tests without subject-specific data and 4.5% over tests using only a limited amount of subject-specific data. While limited to a restricted subset of possible motions and postures, these results provide a significant step in the search for a more robust and accurate ambulatory classification system.
Allen
Felicity R
FR
Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, 2052, Australia.
Ambikairajah
Eliathamby
E
Lovell
Nigel H
NH
Celler
Branko G
BG
D000054:Acceleration; D000058:Accidental Falls / Q000517:prevention & control; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D009068:Movement / Q000502:physiology*; D016011:Normal Distribution; D011187:Posture / Q000502:physiology*; D013997:Time Factors