2025-04-08 ラトガース大学
<関連情報>
- https://www.rutgers.edu/news/tracking-tiny-facial-movements-can-reveal-subtle-emotions-autistic-individuals
- https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1559202/full
デジタルレンズを通して捉えた、自閉症スペクトラム障害における隠れた社会的・感情的能力
Hidden social and emotional competencies in autism spectrum disorders captured through the digital lens
Elizabeth B. Torres,Joe Vero,Neel Drain,Richa Rai,Theodoros Bermperidis
frontiers Published:07 April 2025
DOI:https://doi.org/10.3389/fpsyt.2025.1559202
Background/objectives: The current deficit model of autism leaves us ill-equipped to connect with persons on the spectrum, thus creating disparities and inequalities in all aspects of social exchange in which autistic individuals try to participate. Traditional research models also tend to follow the clinical definition of impairments in social communication and emotions without offering personalized therapeutic help to autistic individuals. There is a critical need to redefine autism with the aim of co-adapting and connecting with this exponentially growing sector of society. Here, we hypothesize that there are social and emotional competencies hidden in the movements’ nuances that escape the naked eye. Further, we posit that we can extract such information using highly scalable means such as videos from smartphones.
Methods: Using a phone/tablet app, we recorded brief face videos from 126 individuals (56 on the spectrum of autism) to assess their facial micro-motions during several emotional probes in relation to their resting state. We extracted the micro-movement spikes (MMSs) from the motion speed along 68 points of the OpenFace grid and empirically determined the continuous family of probability distribution functions best characterizing the MMSs in a maximum likelihood sense. Further, we analyzed the action units across the face to determine their presence and intensity across the cohort.
Results: We find that the continuous Gamma family of probability distribution functions describes best the empirical face speed variability and offers several parameter spaces to automatically classify participants. Unambiguous separation at rest denotes marked differences in stochastic patterns between neurotypicals and autistic individuals amenable to further separate autistic individuals according to the required level of support. Both groups have comparable action units present during emotional probes. They, however, operate within parameter ranges that fall outside our perceptual umwelt and, as such, do not meet our expectations from prior experiences. We cannot detect them.
Conclusions: This work offers new methods to detect hidden facial features and begin the path of augmenting our perception to include those signatures of the autism spectrum that can enhance our capacity for social interactions, communication, and emotional support to meet theirs.
