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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PublisherIEEE Computer Society
Pages225-233
Number of pages9
ISBN (Electronic)9781728125060
StatePublished - Jun 2019
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Duration: Jun 16 2019Jun 20 2019

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2019-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Country/TerritoryUnited States
CityLong Beach
Period6/16/196/20/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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