Spathis, Dimitris

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Brown, C., Chauhan, J., Grammenos, A., Han, J., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., & Mascolo, C. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Phonocardiography and Auscultation TechniquesCOVID-19 diagnosis using AI

Cited by 447

SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data

Tang, C. I., Perez-Pozuelo, I., Spathis, D., Brage, S., Wareham, N., & Mascolo, C. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Context-Aware Activity Recognition SystemsDomain Adaptation and Few-Shot Learning

Cited by 140

Sounds of COVID-19: exploring realistic performance of audio-based digital testing

Han, J., Xia, T., Spathis, D., Bondareva, E., Brown, C., Chauhan, J., Dang, T., Grammenos, A., Hasthanasombat, A., Floto, A., et al. (2022) · npj Digital Medicine

Phonocardiography and Auscultation TechniquesCOVID-19 diagnosis using AI

Cited by 90

The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates

Schuller, B. W., Batliner, A., Bergler, C., Mascolo, C., Han, J., Lefter, I., Kaya, H., Amiriparian, S., Baird, A., Stappen, L., et al. (2021) · Interspeech 2021

Speech Recognition and SynthesisEmotion and Mood Recognition

Cited by 89

Exploring Contrastive Learning in Human Activity Recognition for Healthcare

Tang, C. I., Perez-Pozuelo, I., Spathis, D., & Mascolo, C. (2020) · arXiv

Context-Aware Activity Recognition SystemsDomain Adaptation and Few-Shot Learning

Cited by 67

Wearables, smartphones, and artificial intelligence for digital phenotyping and health

Perez-Pozuelo, I., Spathis, D., Clifton, E. A., & Mascolo, C. (2020) · Elsevier

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 47

Self-supervised transfer learning of physiological representations from free-living wearable data

Spathis, D., Perez-Pozuelo, I., Brage, S., Wareham, N. J., & Mascolo, C. (2021) · Proceedings of the Conference on Health, Inference, and Learning

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 40

Passive mobile sensing and psychological traits for large scale mood prediction

Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., & Rentfrow, J. (2019) · Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare

Mental Health Research TopicsEmotion and Mood Recognition

Cited by 40

Breaking away from labels: The promise of self-supervised machine learning in intelligent health

Spathis, D., Perez-Pozuelo, I., Marques-Fernandez, L., & Mascolo, C. (2022) · Patterns

Machine Learning in HealthcareDomain Adaptation and Few-Shot Learning

Cited by 39

Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science

Context-Aware Activity Recognition SystemsMobile Health and mHealth Applications

Cited by 30

Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments

Spathis, D., Perez-Pozuelo, I., Gonzales, T. I., Wu, Y., Brage, S., Wareham, N., & Mascolo, C. (2022) · npj Digital Medicine

Physical Activity and HealthCardiovascular and exercise physiology

Cited by 30

Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data

Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., & Rentfrow, J. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Emotion and Mood RecognitionMental Health Research Topics

Cited by 30

The first step is the hardest: Pitfalls of Representing and Tokenizing Temporal Data for Large Language Models

Spathis, D., & Kawsar, F. (2023) · arXiv

Topic ModelingMachine Learning in Healthcare

Cited by 2