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The research behind our analytics.

Our analytics methods come from peer-reviewed work by our team and collaborators, on sleep, heart rate and HRV, stress and mental health, biosignal processing and causal modeling. Each paper links to its DOI.

Jump to:SleepDevice validationHeart rate, HRV and respirationStress, affect and mental healthPain assessmentBiosignal processing and signal qualityLongitudinal monitoring and time seriesCausal analysis and machine learningThe platform

Sleep

  1. Kazemi et al. Improved sleep stage predictions by deep learning of photoplethysmogram and respiration patterns. Computers in Biology and Medicine, 2024. doi:10.1016/j.compbiomed.2024.108679
  2. Kazemi et al. Hybrid sleep stage detection using Hidden Markov Chains and Transformers. IEEE EMBC, 2026 (forthcoming).
  3. Azimi et al. Personalized maternal sleep quality assessment: an objective IoT-based longitudinal study. IEEE Access, 2019. doi:10.1109/ACCESS.2019.2927781
  4. Kazemi et al. Can sleep quality attributes be predicted from physical activity in everyday settings?. IEEE EMBC, 2023. doi:10.1109/EMBC40787.2023.10340421

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Device validation

  1. Cao et al. Accuracy assessment of Oura ring nocturnal heart rate and heart rate variability in comparison with electrocardiography in time and frequency domains: comprehensive analysis. Journal of Medical Internet Research, 2022. doi:10.2196/27487
  2. Asgari Mehrabadi et al. Sleep tracking of a commercially available smart ring and smartwatch against medical-grade actigraphy in everyday settings: instrument validation study. JMIR mHealth and uHealth, 2020. doi:10.2196/20465
  3. Sarhaddi et al. A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability. PLOS ONE, 2022. doi:10.1371/journal.pone.0268361
  4. Niela-Vilén et al. Comparison of Oura smart ring against ActiGraph accelerometer for measurement of physical activity and sedentary time in a free-living context. CIN: Computers, Informatics, Nursing, 2022. doi:10.1097/CIN.0000000000000885

Heart rate, HRV and respiration

  1. Sarhaddi et al. Trends in heart rate and heart rate variability during pregnancy and the 3-month postpartum period: continuous monitoring in a free-living context. JMIR mHealth and uHealth, 2022. doi:10.2196/33458
  2. Sharifi-Heris et al. Heart rate variability and pregnancy complications: systematic review. Interactive Journal of Medical Research, 2023. doi:10.2196/44430
  3. Kazemi et al. Respiration rate estimation via smartwatch-based photoplethysmography and accelerometer data: a transfer learning approach. Proc. ACM IMWUT, 2025. doi:10.1145/3712280

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Stress, affect and mental health

  1. Han et al. Objective stress monitoring based on wearable sensors in everyday settings. Journal of Medical Engineering and Technology, 2020. doi:10.1080/03091902.2020.1759707
  2. Tazarv et al. Personalized stress monitoring using wearable sensors in everyday settings. IEEE EMBC, 2021. doi:10.1109/EMBC46164.2021.9630224
  3. Tazarv et al. Active reinforcement learning for personalized stress monitoring in everyday settings. ACM/IEEE CHASE, 2023. doi:10.1145/3580252.3586979
  4. Jafarlou et al. Objective prediction of next-day’s affect using multimodal physiological and behavioral data: algorithm development and validation study. JMIR Formative Research, 2023. doi:10.2196/39425
  5. Yang et al. Integrating wearable sensor data and self-reported diaries for personalized affect forecasting. Smart Health, 2024. doi:10.1016/j.smhl.2024.100464
  6. Jafarlou et al. Objective monitoring of loneliness levels using smart devices: a multi-device approach for mental health applications. PLOS ONE, 2024. doi:10.1371/journal.pone.0298949
  7. Borelli et al. Detection of depressive symptoms in college students using multimodal passive sensing data and light gradient boosting machine: longitudinal pilot study. JMIR Formative Research, 2025. doi:10.2196/67964
  8. Sarhaddi et al. Maternal social loneliness detection using passive sensing through continuous monitoring in everyday settings: longitudinal study. JMIR Formative Research, 2023. doi:10.2196/47950
  9. Wang et al. Differential private federated transfer learning for mental health monitoring in everyday settings: a case study on stress detection. IEEE EMBC, 2024. doi:10.1109/EMBC53108.2024.10782516

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Pain assessment

  1. Kasaeyan Naeini et al. Pain recognition with electrocardiographic features in postoperative patients: method validation study. Journal of Medical Internet Research, 2021. doi:10.2196/25079
  2. Aqajari et al. Pain assessment tool with electrodermal activity for postoperative patients: method validation study. JMIR mHealth and uHealth, 2021. doi:10.2196/25258
  3. Jiang et al. Personalized and adaptive neural networks for pain detection from multi-modal physiological features. Expert Systems with Applications, 2024. doi:10.1016/j.eswa.2023.121082
  4. Subramanian et al. Multimodal pain recognition in postoperative patients: machine learning approach. JMIR Formative Research, 2025. doi:10.2196/67969

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Biosignal processing and signal quality

  1. Feli et al. End-to-end PPG processing pipeline for wearables: from quality assessment and motion artifacts removal to HR/HRV feature extraction. IEEE BIBM, 2023. doi:10.1109/BIBM58861.2023.10385998
  2. Kasaeyan Naeini et al. A deep learning-based PPG quality assessment approach for heart rate and heart rate variability. ACM Transactions on Computing for Healthcare, 2023. doi:10.1145/3616019
  3. Feli et al. An energy-efficient semi-supervised approach for on-device photoplethysmogram signal quality assessment. Smart Health, 2023. doi:10.1016/j.smhl.2023.100390
  4. Afandizadeh Zargari et al. An accurate non-accelerometer-based PPG motion artifact removal technique using CycleGAN. ACM Transactions on Computing for Healthcare, 2023. doi:10.1145/3563949
  5. Feli et al. Multitask learning approach for PPG applications: case studies on signal quality assessment and physiological parameters estimation. Computers in Biology and Medicine, 2025. doi:10.1016/j.compbiomed.2025.109798
  6. Laitala et al. Robust ECG R-peak detection using LSTM. ACM SAC, 2020. doi:10.1145/3341105.3373945
  7. Aqajari et al. pyEDA: an open-source Python toolkit for pre-processing and feature extraction of electrodermal activity. Procedia Computer Science, 2021. doi:10.1016/j.procs.2021.03.021

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Longitudinal monitoring and time series

  1. Wang et al. Personalized graph attention network for multivariate time-series change analysis: a case study on long-term maternal monitoring. ACM SAC, 2023. doi:10.1145/3555776.3577675
  2. Azimi et al. Missing data resilient decision-making for healthcare IoT through personalization: a case study on maternal health. Future Generation Computer Systems, 2019. doi:10.1016/j.future.2019.02.015
  3. Sarhaddi et al. Long-term IoT-based maternal monitoring: system design and evaluation. Sensors, 2021. doi:10.3390/s21072281

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Causal analysis and machine learning

  1. Khatibi et al. Impact of COVID-19 pandemic on sleep including HRV and physical activity as mediators: a causal ML approach. IEEE BSN, 2023. doi:10.1109/BSN58485.2023.10331423
  2. Kazemi et al. Impact of physical activity on quality of life during pregnancy: a causal ML approach. IEEE EMBC, 2024. doi:10.1109/EMBC53108.2024.10782584
  3. Feli et al. Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life. Scientific Reports, 2024. doi:10.1038/s41598-024-70773-0
  4. Yang and Rahmani. Personalized causal graph reasoning for LLMs: an implementation for dietary recommendations. IEEE Journal of Biomedical and Health Informatics, 2025. doi:10.1109/JBHI.2025.3624144

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The platform

  1. Labbaf et al. ZotCare: a flexible, personalizable, and affordable mHealth service provider. Frontiers in Digital Health, 2023. doi:10.3389/fdgth.2023.1253087

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