Publications

@conference{tazarv2021data,

title = {Data Collection and Labeling of Real-Time IoT-Enabled Bio-Signals in Everyday Settings for Mental Health Improvement},

author = {Ali Tazarv and Sina Labbaf and Amir M Rahmani and Nikil Dutt and Marco Levorato},

url = {https://dl.acm.org/doi/10.1145/3462203.3475918},

doi = {10.1145/3462203.3475918},

year  = {2021},

date = {2021-09-01},

urldate = {2021-01-01},

booktitle = {GoodIT '21: Proceedings of the Conference on Information Technology for Social Good},

journal = {arXiv preprint arXiv:2108.01169},

pages = {186–191},

abstract = {Real-time physiological data collection and analysis play a central role in modern well-being applications. Personalized classifiers and detectors have been shown to outperform general classifiers in many contexts. However, building effective personalized classifiers in everyday settings - as opposed to controlled settings - necessitates the online collection of a labeled dataset by interacting with the user. This need leads to several challenges, ranging from building an effective system for the collection of the signals and labels, to developing strategies to interact with the user and building a dataset that represents the many user contexts that occur in daily life. Based on a stress detection use case, this paper (1) builds a system for the real-time collection and analysis of photoplethysmogram, acceleration, gyroscope, and gravity data from a wearable sensor, as well as self-reported stress labels based on Ecological Momentary Assessment (EMA), and (2) collects and analyzes a dataset to extract statistics of users' response to queries and the quality of the collected signals as a function of the context, here defined as the user's activity and the time of the day.},

keywords = {UNITE},

pubstate = {published},

tppubtype = {conference}

}

@conference{tazarv2021personalized,

title = {Personalized Stress Monitoring using Wearable Sensors in Everyday Settings},

author = {Ali Tazarv and Sina Labbaf and Stephanie M Reich and Nikil Dutt and Amir M Rahmani and Marco Levorato},

url = {https://ieeexplore.ieee.org/abstract/document/9630224},

doi = {10.1109/EMBC46164.2021.9630224},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},

journal = {arXiv preprint arXiv:2108.00144},

pages = {7332-7335},

abstract = {Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies. Although several studies have evaluated stress levels in controlled settings, objective stress assessment in everyday settings is still largely under-explored due to challenges arising from confounding contextual factors and limited adherence for self-reports. In this paper, we explore the objective prediction of stress levels in everyday settings based on heart rate (HR) and heart rate variability (HRV) captured via low-cost and easy-to-wear photoplethysmography (PPG) sensors that are widely available on newer smart wearable devices. We present a layered system architecture for personalized stress monitoring that supports a tunable collection of data samples for labeling, and present a method for selecting informative samples from the stream of real-time data for labeling. We captured the stress levels of fourteen volunteers through self-reported questionnaires over periods of between 1-3 months, and explored binary stress detection based on HR and HRV using Machine Learning methods. We observe promising preliminary results given that the dataset is collected in the challenging environments of everyday settings. The binary stress detector is fairly accurate and can detect stressful vs non-stressful samples with a macroF1 score of up to %76. Our study lays the groundwork for more sophisticated labeling strategies that generate context-aware, personalized models that will empower health professionals to provide personalized interventions.},

keywords = {UNITE},

pubstate = {published},

tppubtype = {conference}

}