Abstract

Achieving context-aware computer systems requires that computers can automatically recognize what people are doing. In this work, algorithms are developed and evaluated to detect physical activities from data acquired using five small accelerometers worn simultaneously on different parts of the body. Acceleration data was collected from twenty subjects in both laboratory and semi-naturalistic environments. For semi-naturalistic data, subjects were asked to perform a sequence of everyday tasks outside of the laboratory. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated over 6.71 s sliding windows. Decision table, nearest neighbor, decision tree, and Naive Bayesian classifiers were tested on these features. Classification results using individual training and leave-one-subject-out validation were compared. Leave-one-subject-out validation with decision tree classifiers

Citations