Extracting the Affective Content of Fidgeting in Adults with ADHD via Machine Learning and a Hand-held Soft Tangible Device

Author:

Nasiri Nahid1ORCID,Isbister Katherine2ORCID,Schweitzer Julie B3ORCID,Borden Jared3ORCID,Cottrell Peter S4ORCID,Shapiro Daniel Goodman5ORCID

Affiliation:

1. Electrical and computer engineering, University of California at Santa Cruz, United States

2. Social Emotional Technology Lab, University of California Santa Cruz, United States

3. Department of Psychiatry and Behavioral Sciences and MIND Institute, University of California at Davis, United States

4. Social Emotional Technology Lab, UC Santa Cruz, United States

5. Computational Media, University of California at Santa Cruz, United States

Funder

National Institutes of Child Health and Human Development/NIH

National Institutes of Mental Health/NIH

Publisher

ACM

Reference48 articles.

1. Omar AlZoubi, M Sazzad Hussain, and Rafael A Calvo. 2014. Affect-aware assistive technologies. In Assistive Technology for Cognition. Psychology Press, 47–64.

2. Alice Baird, Andreas Triantafyllopoulos, Sandra Zänkert, Sandra Ottl, Lukas Christ, Lukas Stappen, Julian Konzok, Sarah Sturmbauer, Eva-Maria Meßner, Brigitte M. Kudielka, Nicolas Rohleder, Harald Baumeister, and Björn W. Schuller. 2021. An Evaluation of Speech-Based Recognition of Emotional and Physiological Markers of Stress. Frontiers in Computer Science 3 (2021). https://www.frontiersin.org/articles/10.3389/fcomp.2021.750284

3. Tanja Bänziger, Didier Grandjean, and Klaus R Scherer. 2009. Emotion recognition from expressions in face, voice, and body: the Multimodal Emotion Recognition Test (MERT).Emotion 9, 5 (2009), 691.

4. Russell A Barkley 2006. Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment. (2006).

5. Sentiment analysis in tweets: an assessment study from classical to modern word representation models

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