Key result
A system combining schedule data and ECG-derived heart rate variability successfully estimated stress levels in advance, enabling a tracking robot to provide targeted stress care.
Why the study?
Therapy robots have gained attention for improving depression and related problems, motivating methods to estimate stress levels from vital data and schedule information.
Answers are grounded in this paper and cite their sources.
A novel method combining HRV analysis, schedule data, and a therapy robot can estimate and potentially manage user stress levels.
In this paper, we present an estimation method of stress levels using heart rate variability parameters of vital data. In recent years, there has been much attention paid to the improvement of depression and other problems by therapy robots. In our method, the user registers his/her schedule, time, and expected stress labels in a dedicated smartphone application. The system calculates the stress levels from the content of the schedule and ECG data. The system estimates the stress levels due to the schedule. The implementation of our method makes it possible to estimate the stress levels from the schedule in advance. In addition, this paper uses a robot to represent the relief of sudden stress as an application of this method. By implementing this application, the user can know in advance which schedules he/she will experience stress and can act accordingly, and the robot can provide stress care.
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Sano et al. (2022) studied Stress. Stress estimation method using HRV and schedule data with a tracking mental management robot was evaluated on Estimation of stress levels. A system combining schedule data and ECG-derived heart rate variability successfully estimated stress levels in advance, enabling a tracking robot to provide targeted stress care.
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