We are a highly interdisciplinary team: data science, machine learning, behavioral science, mobile and ubiquitous computing, physics and human computer interaction. We study non-clinical populations (e.g., college students, office workers) as well as clinical populations (e.g. patients with depression, schizophrenia, substance use disorders, Alzheimer, cancers ) in collaboration with researchers in psychology, psychiatry, sleep and circadian disorders, engineering and behavioral science. We are a part of Rice Scalable Health Labs.

[Nov, 2024]: New TMLR paper "AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis".

[Oct, 2024]: New TMLR paper "ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text". 

[July, 2024]: Han Yu successfully defended his PhD! Congratulations, Han!!🎉

[Feb, 2024]: New JMIR Mhealth Uhealth paper. Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

[Jan, 2024]: New IMWUT paper SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks

[Dec, 2023]: New ML4H paper Zero-Shot ECG Diagnosis with Large Language Models and Retrieval-Augmented Generation

[Nov, 2023] New papers at IEEE BHI2023.

Peikun Guo, Han Yu, Sruthi Gopinath Karicheri, Allen Kuncheria, Huiyuan Yang, Siena Blackwell, Zulfi Haneef, Akane Sano, "Empowering Wearable Seizure Forecasting with Scheduled Sampling", 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

Ziang Tang, Zachary King, Alicia Choto Segovia, Han Yu, Gia Braddock, Asami Ito, Ryota Sakamoto, Motomu Shimaoka, Akane Sano, "Burnout Prediction and Analysis in Shift Workers: Counterfactual Explanation Approach", 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) 

[Oct, 2023] New paper about stress detection and counterfactual explanation at ACII2023

Kei Shibuya, Zachary King, Maryam Khalid, Han Yu, Yufei Shen, Khadija Zanna, Ryan Brown, Marzieh Majd, Fagundes Christopher, Akane Sano, "Predicting Stress and Providing Counterfactual Explanations: A Pilot Study on Caregivers", 2023/9, 11th International Conference on Affective Computing & Intelligent Interaction (ACII 2023)

[June, 2024] "Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data", Peikun Guo, Huiyuan Yang, Akane Sano, The 11th IEEE International Conference on Healthcare Informatics (IEEE ICHI 2023)

[May, 2023] New paper about semi-supervised learning models for stress detection was accepted at IMWUT and will be presented at Ubicomp2023.

Han Yu, Akane Sano, "Semi-Supervised Learning for Wearable-based Momentary Stress Detection in the Wild", 2023/6/12, Journal Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

[April, 2023] New paper at Scientific Reports. 

Khalid, M., Sano, A. "Exploiting social graph networks for emotion prediction". Sci Rep 13, 6069 (2023) 

[April, 2023] New paper at IEEE Journal of Biomedical Health Informatics. 

B Lamichhane, J Zhou, A Sano, "Psychotic Relapse Prediction in Schizophrenia Patients Using a Personalized Mobile Sensing-Based Supervised Deep Learning Model" IEEE Journal of Biomedical and Health Informatics

[December, 2022] New papers at NeurIPS 2022 Workshop on Learning from Time Series for Health,

H Yang, H Yu, A Sano, "Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning NeurIPS 2022 Workshop on Learning from Time Series for Health, Spotlight

H Yu, A Sano, "Semi-Supervised Learning and Data Augmentation for Wearable-based Health Monitoring System in the Wild", NeurIPS 2022 Workshop on Learning from Time Series for Health

Z Cao, H Yu, H Yang, A Sano, "PiRL: Participant-Invariant Representation Learning for Healthcare", NeurIPS 2022 Workshop on Learning from Time Series for Health

[October, 2022] New paper at ACII 2022. 

Khadija Zanna, Kusha Sridhar, Han Yu, Akane Sano, "Bias Reducing Multitask Learning on Mental Health Prediction", 10th International Conference on Affective Computing & Intelligent Interaction (ACII 2022) 

[May, 2022] We will organize a workshop/challenge at IEEE EMBC 2022, "Detection of Stress and Mental Health Using Wearable Sensors". We call for challenge participants and paper submissions. Please visit our workshop website.

New paper at IEEE EMBC 2022.

Huiyuan Yang, Han Yu, Kusha Sridhar, Thomas Vaessen, Inez Myin-Germeys, Akane Sano, "More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors", the 44th International Engineering in Medicine and Biology Conference, 2022

[February, 2022] New paper at JMIR mHealth and uHealth.

Joanne Zhou, Bishal Lamichhane, Dror Ben-Zeev, Andrew Campbell, Akane Sano, "Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients", Journal of Medical Internet Research (JMIR) uhealth mhealth, In press, [arxiv] [preprint]

[October, 2021] New paper at  IEEE EMBC 2021.

Ethan N Lyon , Luis H Victor, Akane Sano, "Health Label and Behavioral Feature Prediction Using Bayesian Hierarchical Vector Autoregression Models", International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. [pdf]

[September, 2021] New paper at International Conference on Affective Computing & Intelligent Interaction (ACII 2021) .

Han Yu, Thomas Vaessen, Inez Myin-Germeys, Akane Sano, "Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild", Accepted to 9th International Conference on Affective Computing & Intelligent Interaction (ACII 2021) [arxiv] [code] 

[July, 2021] New Journal paper at ACM Transactions on Computing for Healthcare .

C. Wan, A. W. McHill, E. Klerman, A. Sano. "Sensor-based estimation of dim light melatonin onset (DLMO) using features of two time scales". ACM Transactions on Computing for Healthcare Vol. 2, No. 3. [ACM URL] [arxiv] 

[March, 2021] Our clinical trial protol paper at JMIR protocol

A. Ito-Masui, E. Kawamoto, R. Sakamoto, H. Yu, A. Sano, E. Motomura, H. Tanii, S. Sakano, R. Esumi, H. Imai, M. Shimaoka, "Internet-based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder (Empowered by Wellbeing Prediction): A Pilot Study Protocol", JMIR Protocol, Vol 10, No 3 (2021): March, [JMIR URL]. 

[April, 2021] delighted to receive an NSF career award "Mobile Sensor-Based Adaptive Emotion Prediction and Feedback Delivery"

[November, 2020] We presented two papers at MobiHealth 2020.

"Forecasting Health and Wellbeing for Shift Workers Using Job-role Based Deep Neural Network" (Han Yu et al.) [PDF]

"Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes" (Bishal Lamichhane et al.) [PDF]

[October, 2020] We received an NIH  grant  to measure physical and mental health risks and develop a personalized advice system for dementia spousal caregivers to accomplish everyday tasks and boost their mental health while safely distancing  in collaboration with Dr. Chris Fagundes's team at Rice Psychology. [Link]

[September, 2020] New paper "Using Behavioral Rhythms and Multi-task Learning to Predict Fine Grained Symptoms of Schizophrenia" in Scientific Reports, [PDF].

[September, 2020] Our IMWUT paper "Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress" was presented at Ubicomp 2020.  [PDF] [video].

[July, 2020] Our team received Microsoft pandemic preparedness award for “Design and Evaluation of Intelligent Agent Prototypes for Assistance with COVID-19 work and lifestyle disruptions.”  in collaboration with Drs. Fred Oswald (Rice) and Nidal Moukaddam (Baylor College of Medicine) [link]

[Feb, 2020] Our team received Microsoft Productivity Research Collaboration grant "Unobtrusive Personalized Work Engagement Assistant" in collaboration with Drs. Ashok Veeraraghavan(Rice) and Fred Oswald (Rice)  [link]

[Nov, 2019] We received Rice University Institute of Biosciences and Bioengineering: Hamill Innovation Awards: CraveSupport: Measuring and Intervening Craving Moments in Substance Use Disorders (SUD) using Bio-behavioral Sensor (in collaboration with Drs. Ashutosh Sabharwal (Rice), Nidal Moukaddam, Ramiro Salas (Baylor College of Medicine)). 

[Nov, 2019] We received AMED funding (Japan agency for medical research and development) "Sleep and wellbeing recommendation system for shift workers" in collaboration with Mie University.

[Sept, 2019] We presented "Toward End-to-end Prediction of Future Wellbeing using Deep Sensor Representation Learning"  at International Conference on Affective Computing and Intelligent Interaction (ACII) workshop, Machine Learning for the Diagnosis and Treatment of Affective Disorders (ML4AD). 

[June, 2019] Luis got NSF NRT Bioelectronics Fellowship. Congratulations!

[February, 2022] New paper at JMIR mHealth and uHealth.

Joanne Zhou, Bishal Lamichhane, Dror Ben-Zeev, Andrew Campbell, Akane Sano, "Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients", Journal of Medical Internet Research (JMIR) uhealth mhealth, In press, [arxiv] [preprint]