抄録
In this paper, we aim to develop a stress detection model for caregivers using data collected from a real-world care facility. The importance of stress management has grown, and many physiological datasets and stress detection studies exist for nurses. Caregivers experience various types of stress, including interpersonal issues and assisting with toileting, and are physically active throughout the day, moving around the floor and performing tasks like transfer assistance. These diverse stressors and physical activity can significantly affect stress detection using wearable sensors. However, sufficient data collection in caregiving settings is not being conducted compared to other fields (e.g., nursing, doctor). In this study, we conduct a data collection experiment and evaluate the results of stress detection machine learning using that data. In the data collection, we create 8-day dataset from four caregivers that includes care record data, wearable sensor data, and self-reported stress labels. Next, we extracted statistical features from the time-series data and performed stress detection using a Random Forest model. As a result, we achieved a maximum classification accuracy of 72%. While data augmentation improved the detection of minority classes such as High Stress, it also lowered the overall classification performance, revealing a trade-off that remains a challenge. Nevertheless, this dataset makes it possible to analyze caregiver stress in relation to specific care activities, representing an important step toward stress-aware support in the caregiving domain.
https://ror.org/02278tr80 
