Personalized machine learning for robot perception of affect and engagement in autism therapy
Open accessAbstract
Personalized machine learning enables robot perception of children’s affective states and engagement during robot-assisted autism therapy.
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A personalised deep learning framework can adapt robot perception of children's affective states and engagement to different cultures and individuals.
“A personalised deep learning framework can adapt robot perception of children's affective states and engagement to different cultures and individuals.”
From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [68]
The personalised deep learning system achieved an average agreement of ~60% with human experts to estimate effect and engagement.
“Achieved an average agreement of ~60% with human experts to estimate effect and engagement.”
From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [68]
Cites
Diagnostic and Statistical Manual of Mental Disorders
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Greedy function approximation: A gradient boosting machine
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Intraclass correlations: Uses in assessing rater reliability.
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Does the autistic child have a “theory of mind” ?
Baron-Cohen, S., Leslie, A. M., & Frith, U. (1985) · Cognition · doi:10.1016/0010-0277(85)90022-8 · Cited by 8,599 · cited 1× · see citing works
“These steps are adopted from the theory ofmind concept (14), designed to teach perspective-taking (“social imagination”)—a challenge for many children with ASC.”
BackgroundRealtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539 · see citing works
What can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131 · cited 1× · see citing works
“nt advances in ML and data collection, using unobtrusive sensors such as cameras and microphones, and wearable technology for measurement of autonomic physiology, have paved a way for such technology (61), however, little progress has been done so far (62). To this end, we introduced a novel personalized ML framework…”
MethodPersonalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health
Taylor, S., Jaques, N., Nosakhare, E., Sano, A., & Picard, R. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2784832 · Cited by 251 · see citing works
Self-citationAutomatic analysis of affective postures and body motion to detect engagement with a game companion,
Sanghvi, J., Castellano, G., Leite, I., Pereira, A., McOwan, P. W., & Paiva, A. (2011) · Proceedings of the 6th international conference on Human-robot interaction · doi:10.1145/1957656.1957781 · Cited by 250 · see citing works
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Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements
Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019) · Psychological Science in the Public Interest · doi:10.1177/1529100619832930 · Cited by 1,748 · see citing works
Barua, P. D., Vicnesh, J., Gururajan, R., Oh, S. L., Palmer, E., Azizan, M. M., Kadri, N. A., & Acharya, U. R. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031192 · Cited by 270 · see citing works
Advanced Applications of Industrial Robotics: New Trends and Possibilities
Dzedzickis, A., Subačiūtė-Žemaitienė, J., Šutinys, E., Samukaitė-Bubnienė, U., & Bučinskas, V. (2021) · Applied Sciences · doi:10.3390/app12010135 · Cited by 228 · see citing works
Fadl Federated-autonomous deep learning for distributed electronic health record
Liu, D., Miller, T., Sayeed, R., & Mandl, K. D. (2018) · arXiv · doi:10.48550/arxiv.1811.11400 · Cited by 65 · see citing works
Robotics in the Construction Sector: Trends, Advances, and Challenges
Liu, Y., A.H., A., Haron, N. A., N.A., B., & Wang, H. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02104-4 · Cited by 60 · see citing works
Personalised depression forecasting using mobile sensor data and ecological momentary assessment
Kathan, A., Harrer, M., Küster, L., Triantafyllopoulos, A., He, X., Milling, M., Gerczuk, M., Yan, T., Rajamani, S. T., Heber, E., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.964582 · Cited by 47 · see citing works
Self-citation
- Authors
- Rudovic, O., Lee, J., Dai, M., Schuller, B., & Picard, R. W.
- Affiliations
- MIT Media Lab (US)
- Chubu University (JP)
- University of Augsburg (DE)
- Corresponding author
- Rudovic, Ognjen
- Venue
- Science Robotics
- Published
- 2018-06-20
- Volume
- 3
- Issue
- 19
- ISSN
- 2470-9476 (ISSN-L)
- OpenAlex
- W2785722081
- Cited by
- 316
- References
- 8
- Citations by year
- 2026: 33 · 2025: 24 · 2024: 39 · 2023: 38 · 2022: 63 · 2021: 37 · 2020: 43 · 2019: 27 · 2018: 12
- FWCI
- 38.06
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Science Robotics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OPUS (Augsburg University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- EU Horizon 2020 (688835 (DE-ENIGMA))
- MEXT Grantin- Aid for Young Scientists B Grant (16763279)
- Chubu University Grant I Grant (27IS04I)
- EU HORIZON 2020 - Marie Skłodowska-Curie Individual Fellowship (701236 (ENGAGEME))
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Keywords
- autism spectrum disorder, robot-assisted therapy, engagement detection, personalized machine learning, social robots, multimodal data, autonomic physiology, children, cross-cultural comparison
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