204 citations · 489 across the 9 of their papers we have counts for
9 papers
Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing
Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +24
Objective: This study aimed to explore the associations between depression severity and wearable-measured circadian rhythms, accounting for seasonal impacts and quantifying seasona…
Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis
Shaoxiong Sun, Amos A. Folarin, Yuezhou Zhang +28
A number of challenges exist for the analysis of mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an accepta…
More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors
Huiyuan Yang, Han Yu, Kusha Sridhar +3
Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focuse…
Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings
Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +27
Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of dai…
The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions
Shaoxiong Sun, Amos A Folarin, Yuezhou Zhang +25
Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute w…
Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild
Han Yu, Thomas Vaessen, Inez Myin-Germeys +1
Multimodal wearable physiological data in daily life have been used to estimate self-reported stress labels. However, missing data modalities in data collection makes it challengin…
Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study
Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +21
The Bluetooth sensor embedded in mobile phones provides an unobtrusive, continuous, and cost-efficient means to capture individuals' proximity information, such as the nearby Bluet…
The Relationship between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multi-centre Longitudinal Observational Study
Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +19
Research in mental health has implicated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography, is not suitable for long-term, continu…
Using smartphones and wearable devices to monitor behavioural changes during COVID-19
Shaoxiong Sun, Amos Folarin, Yatharth Ranjan +23
We aimed to explore the utility of the recently developed open-source mobile health platform RADAR-base as a toolbox to rapidly test the effect and response to NPIs aimed at limiti…