Search Results for author:
Found 6 papers, 2 papers with code
Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis
Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model.
More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors
For example, although combining bio-signals from multiple sensors (i. e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context.
Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings
The gait cadence of faster steps (75th percentile) over a long-term period has a significant negative association with the depression symptom severity of this period in both datasets.
The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions
In this work, we extracted 96 activity features in different temporal granularities (from minute-level to day-level) and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10-month duration.
Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild
Compared to the baseline method using the samples with complete modalities, the performance of the MFN improved by 1. 6% in f1-scores.
Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study
We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features.
