Stress Detection Using Bio-Signal Processing: An Application of IoT and Machine Learning for Old Age Home Residents

Stress is a multifaceted physiological and psychological response that impacts health in diverse ways. This work introduces an IoT- and ML-based wearable stress detection prototype system for elderly care. The prototype developed utilizes Heart Rate, Skin Temperature, and GSR (Galvanic Skin Response) sensors, integrated with data input for real-time analysis. Among different prediction models, Random Forest was found to achieve the highest performance measured in terms of Accuracy (95.06%), Precision (95.22%), Recall (95.06%) and F1-Score (94.38%) and hence was employed for the stress-prediction purpose. Validated on old age home residents (SHEOWS, New Delhi), the device demonstrated satisfactory performance, enabling personalized care and improved stress management through precise, data-driven insights. This is preliminary research which needs to be extended appropriately in the future for further improvements and will work as an input for stress-reduction techniques for elderly people.