A Segmentation Scheme for Knowledge Discovery in Human Activity Spotting.

Abstract

Spotting the target activity in a sequence of activities and transitions without applying a predetermined window size is a challenging task. The sliding window method, which is the typical approach in segmenting the continuous data stream, operates with optimal segment sizes chosen considering the type and duration of the activities. Nevertheless, activity type and duration are usually unknown to the detection unit in practice. In this study, we proposed the nonpredetermined size windowing (NSW) scheme to spot the target activity performed in a sequence of unseen activities. NSW is built on classifying progress-based features in multilayer training and prediction stages where the time-domain progress is expressed in terms of polynomials. Thus, it operates without incorporating the information regarding duration and type of the activities. We verified our method with a proof-of-concept use case where data are acquired by a single wrist-worn 3-D accelerometer. We compared our method against fixed size windowing performed with varying window sizes and feature extraction schemes; windowed energy, peak frequency, Shannon entropy, and wavelet entropy. Our method outperforms the compared schemes, reaching a median accuracy of 89% and a median true positive rate of 1 in both intrasubject and intersubject cases based on an independent test set where each test instance contains a sequence of nine different activities.