<![CDATA[Mental health has become a major global issue, particularly after the COVID-19 pandemic, which significantly increased the prevalence of psychological disorders. Early detection of stress and other mental health problems remains a major challenge, as traditional methods are generally subjective and unable to provide real-time results. This study aims to design and test a wearable sensor based on Heart Rate Variability (HRV) as a physiological indicator for detecting stress levels. The research employed an experimental approach through the development of a wearable sensor prototype equipped with a stress detection algorithm based on HRV analysis, including both time-domain and frequency-domain parameters. The prototype was tested on 100 respondents with varying stress levels under controlled conditions. Instruments used in this study included the HRV sensor prototype, psychological questionnaires, and standard validation devices. Data were analyzed by comparing the sensor detection results with respondents’ psychological data and calculating prediction accuracy. The findings showed that the wearable sensor was able to predict stress conditions with an accuracy rate of 80%. The distribution of sensor detection results was generally consistent with psychological data, especially in the low-stress category, although slight deviations were observed in moderate and high-stress categories. These results demonstrate that an HRV-based wearable sensor can serve as a practical and non-invasive tool to monitor mental conditions in real time. The implications of this research highlight the potential of wearable technology as an innovative solution for mental health monitoring, both for individual use and as support for healthcare systems. Therefore, this study contributes to the development of adaptive and responsive health technologies in addressing global mental health challenges.]]>
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