Development and Validation of an Unobtrusive Automatic Sleep Quality Assessment Index (ASQI) for Elderly Individuals

This study presents the development and validation of an unobtrusive automatic sleep quality assessment index (ASQI) designed for elderly individuals. The proposed method utilizes features such as sleep duration, sleep latency, and sleep efficiency, calculated from physiological data—heart rate, respiratory rate, body movements, and bed-exit behavior—captured by a non-contact bed sensor system installed in home environments. Based on these parameters, a six-component sleep quality index was constructed to objectively evaluate nightly sleep. To assess the reliability and validity of ASQI, sleep data were collected from eleven elderly participants over a one-year period. Results showed strong test–retest reliability (r=0.91, p<0.001) and moderate correlation with the widely used Pittsburgh Sleep Quality Index (PSQI) (r=0.52, p<0.05). Furthermore, ASQI successfully differentiated between self-reported good and poor sleepers, achieving a classification accuracy of 85.7%, with a sensitivity of 66.7% and specificity of 93.3%. These findings demonstrate that the ASQI system is a practical and scalable tool for continuous, home-based sleep monitoring in older populations. Its non-intrusive design and objective scoring make it well-suited for personalized sleep management and early detection of sleep-related issues. This work contributes to the growing field of unobtrusive health monitoring and highlights the potential of sensor-based systems in elderly care.