Aveid

Parekh, Viral; Foong, Pin Sym; Zhao, Shengdong; Subramanian, Ramanathan

doi:10.1145/3172944.3173010

Cited by 14 publications

(2 citation statements)

References 13 publications

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“…Another study from Parekh et al (2018) developed a video system for measuring engagement in patients with dementia, which uses deep-learning based computer vision algorithms to evaluate their engagement in an activity to provide behavior analytics based on facial expression and gaze analysis. Ground truth was extracted through scoring performed by human annotators by classifying engagement states in terms of attention and attitude.…”

Section: Related Workmentioning

confidence: 99%

Toward an Automated Measure of Social Engagement for Children With Autism Spectrum Disorder—A Personalized Computational Modeling Approach

2020

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Social engagement is a key indicator of an individual's socio-emotional and cognitive states. For a child with Autism Spectrum Disorder (ASD), this serves as an important factor in assessing the quality of the interactions and interventions. So far, qualitative measures of social engagement have been used extensively in research and in practice, but a reliable, objective, and quantitative measure is yet to be widely accepted and utilized. In this paper, we present our work on the development of a framework for the automated measurement of social engagement in children with ASD that can be utilized in real-world settings for the long-term clinical monitoring of a child's social behaviors as well as for the evaluation of the intervention methods being used. We present a computational modeling approach to derive the social engagement metric based on a user study with children between the ages of 4 and 12 years. The study was conducted within a child-robot interaction setting that targets sensory processing skills in children. We collected video, audio and motion-tracking data from the subjects and used them to generate personalized models of social engagement by training a multi-channel and multi-layer convolutional neural network. We then evaluated the performance of this network by comparing it with traditional classifiers and assessed its limitations, followed by discussions on the next steps toward finding a comprehensive and accurate metric for social engagement in ASD.

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Section: Related Workmentioning

confidence: 99%

Toward an Automated Measure of Social Engagement for Children With Autism Spectrum Disorder—A Personalized Computational Modeling Approach

2020

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“…Many studies have focused on non-verbal behavioural cues, such as body gestures [23,24], facial expressions [9,13,21], their combination [34] and speech features [12,22,36] as biomarkers for depression diagnosis and rehabilitation utilising computational tools [37]. Head motion patterns have nevertheless received little attention.…”

Section: Depression Detection Via Head Motion Cuesmentioning

confidence: 99%

Explainable Depression Detection via Head Motion Patterns

Gahalawat,

Fernandez Rojas,

Guha

et al. 2023

International Conference on Multimodal Interaction

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While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker. This study demonstrates the utility of fundamental head-motion units, termed kinemes, for depression detection by adopting two distinct approaches, and employing distinctive features: (a) discovering kinemes from head motion data corresponding to both depressed patients and healthy controls, and (b) learning kineme patterns only from healthy controls, and computing statistics derived from reconstruction errors for both the patient and control classes. Employing machine learning methods, we evaluate depression classification performance on the BlackDog and AVEC2013 datasets. Our findings indicate that: (1) head motion patterns are effective biomarkers for detecting depressive symptoms, and (2) explanatory kineme patterns consistent with prior findings can be observed for the two classes. Overall, we achieve peak F1 scores of 0.79 and 0.82, respectively, over BlackDog and AVEC2013 for binary classification over episodic thin-slices, and a peak F1 of 0.72 over videos for AVEC2013.

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A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia Among British Sign Language Users

Liang

Angelopoulou

Kapetanios

et al. 2020

Lecture Notes in Computer Science

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The ageing population trend is correlated with an increased prevalence of acquired cognitive impairments such as dementia. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support and appropriate medication. Researchers are working urgently to develop effective technological tools that can help doctors undertake early identification of cognitive disorder. In particular, screening for dementia in ageing Deaf signers of British Sign Language (BSL) poses additional challenges as the diagnostic process is bound up with conditions such as quality and availability of interpreters, as well as appropriate questionnaires and cognitive tests. On the other hand, deep learning based approaches for image and video analysis and understanding are promising, particularly the adoption of Convolutional Neural Network (CNN), which require large amounts of training data. In this paper, however, we demonstrate novelty in the following way: a) a multi-modal machine learning based automatic recognition toolkit for early stages of dementia among BSL users in that features from several parts of the body contributing to the sign envelope, e.g., hand-arm movements and facial expressions, are combined, b) universality in that it is possible to apply our technique to users of any sign language, since it is language independent, c) given the trade-off between complexity and accuracy of machine learning (ML) prediction models as well as the limited amount of training and testing data being available, we show that our approach is not over-fitted and has the potential to scale up.

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Aveid

Cited by 14 publications

References 13 publications

Toward an Automated Measure of Social Engagement for Children With Autism Spectrum Disorder—A Personalized Computational Modeling Approach

Toward an Automated Measure of Social Engagement for Children With Autism Spectrum Disorder—A Personalized Computational Modeling Approach

Explainable Depression Detection via Head Motion Patterns

A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia Among British Sign Language Users

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