Stress and Anxiety Measurement "In-the-Wild" Using Quality-aware Multi-scale HRV Features

More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors

Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common choice in many applications, but may not always be feasible in real-world scenarios. For example, although combining biosignals 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. To solve the challenges, we propose an effective more to less (M2L) learning framework to improve testing performance with reduced sensors through leveraging the complementary information of multiple modalities during training. More specifically, different sensors may carry different but complementary information, and our model is designed to enforce collaborations among different modalities, where positive knowledge transfer is encouraged and negative knowledge transfer is suppressed, so that better representation is learned for individual modalities. Our experimental results show that our framework achieves comparable performance when compared with the full modalities. Our code and results will be available at https://github.com/comp-well-org/More2Less.git.

Estimating Physical/Mental Health Condition Using Heart Rate Data from a Wearable Device

We propose an estimation method of subjects' physical/mental health condition from their heart rate (HR) and evaluate it on the newly collected data including 25 million points over 97 participants. The accurate health condition estimation is important for an employee's mental health care and an objective understanding of our condition. For the estimation, the heart rate variability (HRV) has been widely used, but there are some technical difficulties with measuring the HRV, such as maintaining a good quality of data for a long period of time. Here, we predict the subjects' physical/mental health only from the HR measured by Fitbit instead of the HRV. We first measured more than 25 million points of HR and steps data from 97 participants over 3 months using the Fitbit Inspire HR^TM. We also conducted questionnaires to check their physical conditions each day. We then predict their condition by focusing on the inactive period of HR and applying the support vector machine to the preprocessed data. The best balanced accuracy of our method achieved 0.582, which was higher than the state-of-the-art method with HRV whose accuracy is 0.565.

TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers

We present a novel longitudinal multimodal corpus of physiological and behavioral data collected from direct clinical providers in a hospital workplace. We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings. We collected behavioral and physiological data from n = 212 participants through Internet-of-Things Bluetooth data hubs, wearable sensors (including a wristband, a biometrics-tracking garment, a smartphone, and an audio-feature recorder), together with a battery of surveys to assess personality traits, behavioral states, job performance, and well-being over time. Besides the default use of the data set, we envision several novel research opportunities and potential applications, including multi-modal and multi-task behavioral modeling, authentication through biometrics, and privacy-aware and privacy-preserving machine learning.

Measurement(s)	Overall Sleep Quality Rating • Step Unit of Distance • Speech • Mean Heart Rate • Proximity • Electrocardiogram Sequence • heart rate variability measurement • Respiratory Rate • physical activity measurement • light • door motion • Changes in Ambient Temperature in Medical Device Environment • humidity • Overall Emotional Well-Being • Stress • psychological flexibility • work-related acceptance • work engagement • psychological capital • intelligence • job performance • organizational citizenship behavior • counter-productive work behavior • personality trait measurement • Negative affectivity • positive affectivity • anxiety-related behavior trait • Alcohol Use History • Overall Health Rating During Past Week
Technology Type(s)	photoplethysmography • Accelerometer • Microphone Device • Bluetooth-enabled Activity Monitor • electrocardiogram • Sensor Device • Photodetector Device • Temperature Sensor Device • questionnaire • Multidimensional Psychological Flexibility Inventory (MPFI) • Utrecht work engagement scale • survey method • individual task proficiency • Search Results Web results Organizational Citizenship Behavior Checklist • big five inventory • Positive and Negative Affect Schedule (PANAS-X) • State-Trait Anxiety Inventory
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Environment	hospital

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12465101