Evaluation of Event-Based and Timing Features from Head Movement and Eye Activity for Wearable Task Transition Detection

Human task transition detection has previously focused on body movement signals and corresponding statistical features, however, these may be less effective for detecting transitions between mental tasks. Eye features, which have been shown effective in cognitive task processing, have rarely been adopted in physical task analysis or activity transition, and are investigated herein. It is also of interest to tokenize time series signals into meaningful word-like event-based features, which provides a novel and more interpretable alternative to statistical features and access to timing information between events, which has the potential to capture human activity patterns but has not been investigated for task transition detection. In this paper, we combine head movement and eye features and analyze their contributions; apply event-based features to recognize task transition detection using natural language processing methods; develop novel event-based features to capture the change in pupil size; represent the timing information between consecutive events; and reveal insights into event-based and timing features. Evaluations on a novel semi-naturalistic dataset show that automatic task transition can be quite reliably detected using head movement and eye activity from wearable sensors, that the classification performance of fused event-based and timing features is comparable with that of statistical features, and for eye features only, both the proposed event-based features and timing features significantly outperform the statistical features. The fusion of event-based features and timing features also slightly improves the classification performance over systems based on either feature set alone.