Classification of posture and activities by using decision trees.
Obesity prevention and treatment as well as healthy life style recommendation requires the estimation of everyday physical activity. Monitoring posture allocations and activities with sensor systems is an effective method to achieve the goal. However, at present, most devices available rely on multiple sensors distributed on the body, which might be too obtrusive for everyday use. In this study, data was collected from a wearable shoe sensor system (SmartShoe) and a decision tree algorithm was applied for classification with high computational accuracy. The dataset was collected from 9 individual subjects performing 6 different activities--sitting, standing, walking, cycling, and stairs ascent/descent. Statistical features were calculated and the classification with decision tree classifier was performed, after which, advanced boosting algorithm was applied. The computational accuracy is as high as 98.85% without boosting, and 98.90% after boosting. Additionally, the simple tree structure provides a direct approach to simplify the feature set.
Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA.
Ting
T
Zhang
Wenlong
W
Tang
Edward S
ES
Sazonov
D056044:Actigraphy / Q000295:instrumentation*; D000293:Adolescent; D000328:Adult; D003661:Decision Support Techniques; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005528:Foot / Q000502:physiology*; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D009068:Movement / Q000502:physiology; D011187:Posture / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012775:Shoes; D014160:Transducers, Pressure; D055815:Young Adult