Categorical Timeline Allocation and Alignment for Diagnostic Head Movement Tracking Feature Analysis

Mitsunori Ogihara, Zakia Hammal, Katherine B. Martin, Jeffrey F. Cohn, Justine Cassell, Gang Ren, Daniel S. Messinger; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 43-51

Atypical head movement pattern characterization is a potentially important cue for identifying children with autism spectrum disorder. In this paper, we implemented a computational framework for extracting the temporal patterns of head movement and utilizing the imbalance of temporal pattern distribution between diagnostic categories (e.g., children with or without autism spectrum disorder) as potential diagnostic cues. The timeline analysis results show a large number of temporal patterns with significant imbalances between diagnostic categories. The temporal patterns show strong classification power on discriminative and predictive analysis metrics. The long time-span temporal patterns (e.g., patterns spanning 15-30 sec.) exhibit stronger discriminative capabilities compared with the temporal patterns with relatively shorter time spans. Temporal patterns with high coverage ratios (existing in a large portion of the video durations) also show high discriminative capacity.