title: A comparison of activity classification in younger and older cohorts using a smartphone.
abstract: Automatic recognition of human activity is useful as a means of estimating energy expenditure and has potential for use in fall detection and prediction. The emergence of the smartphone as a ubiquitous device presents an opportunity to utilize its embedded sensors, computational power and data connectivity as a platform for continuous health monitoring. In the study described herein, 37 older people (83.9  ±  3.4 years) performed a series of activities of daily living (ADLs) while a smartphone (containing a triaxial accelerometer, triaxial gyroscope and barometric pressure sensor) was placed in the front pocket of their trousers. These results are compared to a similar trial conducted previously in which 20 young people (21.9  ±  1.65 years) were asked to perform the same ADLs using the same smartphone (again in the front pocket of their trousers).In each trial, the participants were asked to perform several activities (standing, sitting, lying, walking on level ground, up and down staircases, and riding an elevator up and down) in a free-living environment. During each acquisition session, the internal sensor signals were recorded and subsequently used to develop activity classifiers based on a decision tree algorithm that classified ADL in epochs of ~1.25 s. When training and testing with the younger cohort, using a leave-one-out cross validation procedure, a total classification sensitivity of 80.9% ± 9.57% ([Formula: see text] = 0.75  ±  0.12) was obtained. Retraining and testing on the older cohort, again using cross validation, gives a comparable total class sensitivity of 82.0% ± 8.88% ([Formula: see text] =0.74  ±  0.12).When trained with the younger group and tested on the older group, a total class sensitivity of 69.2% ± 24.8% (95% confidence interval [69.6%, 70.6%]) and [Formula: see text] = 0.60  ±  0.27 (95% confidence interval [0.58, 0.59]) was obtained. When trained on the older group and tested on the younger group, a total class sensitivity of 80.5% ± 6.80% (95% confidence interval [79.0%, 80.6%]) and [Formula: see text] = 0.74  ±  0.08 (95% confidence interval [0.73, 0.75]) was obtained.An instance of the decision tree classifier developed was implemented on the smartphone as a software application. It was capable of performing real-time activity classification for a period of 17 h on a single battery charge, illustrating that smartphone technology provides a viable platform on which to perform long-term activity monitoring.
authors.affiliation: Graduate School of Biomedical Engineering, UNSW Australia, Sydney, NSW 2052, Australia.
authors.forename: Michael B
authors.initials: MB
authors.lastname: Del Rosario
authors.forename: Kejia
authors.initials: K
authors.lastname: Wang
authors.forename: Jingjing
authors.initials: J
authors.lastname: Wang
authors.forename: Ying
authors.initials: Y
authors.lastname: Liu
authors.forename: Matthew
authors.initials: M
authors.lastname: Brodie
authors.forename: Kim
authors.initials: K
authors.lastname: Delbaere
authors.forename: Nigel H
authors.initials: NH
authors.lastname: Lovell
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Lord
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Redmond
mesh_terms: D000203:Activities of Daily Living; D000369:Aged, 80 and over; D000465:Algorithms; D040421:Cell Phone; D015331:Cohort Studies; D003625:Data Collection; D003663:Decision Trees; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D011312:Pressure; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000379:methods*; D013997:Time Factors; D055815:Young Adult