Publications

You can also find my articles on my Google Scholar profile.

Fusing wearable biosensors with artificial intelligence for mental health monitoring: A systematic review Permalink

Published in MDPI, 2025

The development of digital instruments for mental health monitoring using biosensor data from wearable devices can enable remote, longitudinal, and objective quantitative benchmarks. To survey developments and trends in this field, we conducted a systematic review of artificial intelligence (AI) models using data from wearable biosensors to predict mental health conditions and symptoms. Following PRISMA guidelines, we identified 48 studies using a variety of wearable and smartphone biosensors including heart rate, heart rate variability (HRV), electrodermal activity/galvanic skin response (EDA/GSR), and digital proxies for biosignals such as accelerometry, location, audio, and usage metadata. We observed several technical and methodological challenges across studies in this field, including lack of ecological validity, data heterogeneity, small sample sizes, and battery drainage issues. We outline several corresponding opportunities for advancement in the field of AI-driven biosensing for mental health.

Recommended citation: Kargarandehkordi A, Li S, Lin K, Phillips KT, Benzo RM, Washington P. Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review. Biosensors. 2025; 15(4):202. https://doi.org/10.3390/bios15040202 https://www.mdpi.com/2079-6374/15/4/202

Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing Permalink

Published in MDPI, 2024

Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addressing severe between-subject data heterogeneity have hampered the adaptation of machine learning approaches for substance use detection, necessitating more robust technological solutions. We tested the utility of personalized machine learning using participant-specific convolutional neural networks (CNNs) enhanced with self-supervised learning (SSL) to detect drug use. In a pilot feasibility study, we collected data from 9 participants using Fitbit Charge 5 devices, supplemented by ecological momentary assessments to collect real-time labels of substance use. We implemented a baseline 1D-CNN model with traditional supervised learning and an experimental SSL-enhanced model to improve individualized feature extraction under limited label conditions. Results: Among the 9 participants, we achieved an average area under the receiver operating characteristic curve score across participants of 0.695 for the supervised CNNs and 0.729 for the SSL models. Strategic selection of an optimal threshold enabled us to optimize either sensitivity or specificity while maintaining reasonable performance for the other metric. Conclusion: These findings suggest that Fitbit data have the potential to enhance substance use monitoring systems. However, the small sample size in this study limits its generalizability to diverse populations, so we call for future research that explores SSL-powered personalization at a larger scale.

Recommended citation: Li, S., Fan, C., Kargarandehkordi, A., Sun, Y., Slade, C., Jaiswal, A., Benzo, R. M., Phillips, K. T., & Washington, P. (2024). Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing. AI, 5(4), 2725-2738. https://doi.org/10.3390/ai5040131 https://www.mdpi.com/2673-2688/5/4/131

Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Proposal for a Prospective Cohort Study Permalink

Published in JMIR, 2024

The aim of this study is to leverage machine learning (ML) algorithms for real-time predictions of stress-induced BP spikes using consumer wearable devices such as Fitbit, providing actionable insights to both patients and clinicians to improve diagnostics and enable proactive health monitoring. This study also seeks to address the significant challenges in identifying specific deleterious behaviors associated with stress-induced hypertension through the development of personalized artificial intelligence models for individual patients, departing from the conventional approach of using generalized models.

Recommended citation: Kargarandehkordi, A., Slade, C., & Washington, P. (2024). Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Proposal for a Prospective Cohort Study. JMIR Research Protocols, 13(1), e55615. https://www.researchprotocols.org/2024/1/e55615/

TikTokActions: A TikTok-Derived Video Dataset for Human Action Recognition Permalink

Published in arxiv, 2024

We release this dataset as a valuable resource for building domain-specific foundation models for human movement modeling tasks such as action recognition.

Recommended citation: Qian, Y., Sun, Y., Kargarandehkordi, A., Mutlu, O. C., Surabhi, S., Chen, P., ... & Washington, P. (2024). TikTokActions: A TikTok-Derived Video Dataset for Human Action Recognition. arXiv preprint arXiv:2402.08875. https://arxiv.org/abs/2402.08875

Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study Permalink

Published in JMIR, 2024

In this study, we aimed to understand the feasibility of continuous remote digital monitoring and ecological momentary assessments in Native Hawaiian, Filipino, and Pacific Islander communities in Hawaii by curating a novel data set of longitudinal Fitbit (Fitbit Inc) biosignals with the corresponding craving and substance use labels. We also aimed to develop personalized AI models that predict methamphetamine craving events in real time using wearable sensor data.

Recommended citation: Sun, Y., Kargarandehkordi, A., Slade, C., Jaiswal, A., Busch, G., Guerrero, A., ... & Washington, P. (2024). Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study. JMIR Research Protocols, 13(1), e46493. https://www.researchprotocols.org/2024/1/e46493

Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study Permalink

Published in Applied Sciences Journal, 2024

Our study delves into the concept of model personalization in emotion recognition, moving away from the one-size-fits-all approach. We conducted a series of experiments using the Emognition dataset, comprising physiological and video data of human subjects expressing various emotions, to investigate this personalized approach to affective computing.

Recommended citation: Kargarandehkordi, A., Kaisti, M., & Washington, P. (2024). Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study. Applied Sciences, 14(4), 1337. https://aliknd.github.io/files/applsci-2783038-final.pdf

Computer Vision Estimation of Stress and Anxiety Using a Gamified Mobile-based Ecological Momentary Assessment and Deep Learning: Research Protocol Permalink

Published in medRxiv, 2023

Stress and anxiety can contribute to the development of major health problems such as heart disease, depression, and obesity. Due to its subjective nature, it is challenging to precisely measure human affect by relying on automated approaches. We therefore propose a personalized prediction framework fine-tuned for each participant in lieu of the traditional “one-size-fits-all” machine learning approach.

Recommended citation: Kargarandehkordi, A., & Washington, P. (2023). Computer Vision Estimation of Stress and Anxiety Using a Gamified Mobile-based Ecological Momentary Assessment and Deep Learning: Research Protocol. medRxiv, 2023-04. https://www.medrxiv.org/content/10.1101/2023.04.28.23289168v1

Computer Vision Estimation of Emotion Reaction Intensity in the Wild Permalink

Published in arxiv, 2023

Developing computer vision models for automatic recognition of emotion expression can aid in a variety of domains, including robotics, digital behavioral healthcare, and media analytics.

Recommended citation: Qian, Y., Kargarandehkordi, A., Mutlu, O. C., Surabhi, S., Honarmand, M., Wall, D. P., & Washington, P. (2023). Computer Vision Estimation of Emotion Reaction Intensity in the Wild. arXiv preprint arXiv:2303.10741. https://arxiv.org/abs/2303.10741

Ali Kargarandehkordi

Publications