In-situ wearable-based dataset of continuous heart rate variability monitoring accompanied by sleep diaries

In-situ wearable-based dataset of continuous heart rate variability monitoring accompanied by sleep diaries integrates continuous physiological states and motion signals collected via smartwatches from 49 healthy individuals (mean age: 28.35±5.87, including 51% females) over four weeks. The recordings were sampled every 100 ms (10 Hz), allowing for short-term HRV computation for each 5-minute segment of raw data. We validated the collected signals by examining collection frequency, analyzing the correlation between smartwatch sensor data and computed HRV, and comparing HRV and sleep-related feature distributions with existing literature. Alongside wearable recordings, we obtained daily sleep diaries and biweekly clinical questionnaire results to assess participants' mental disorders, such as anxiety, depression, and insomnia. The dataset aims to benchmark in-the-wild HRV recordings and support researchers in developing algorithms to predict mental health and sleep patterns using health indicators.