Extending Healthy Life Expectancy: Frailty Detection Model Using Smart Meter and Machine Learning.

Recently, smart meters have been widely used as social infrastructure, making it easy to obtain electricity power consumption data. Electricity power consumption data has high potential in healthcare since it reflects various activities in daily life. We have been focusing on the potential of smart meters and studying frailty detection model using electricity power consumption data; frailty is a condition of reduced function and health in the elderly, also said to be reversible, which means the elderly can return to robust conditions by implementing appropriate interventions. Therefore, detecting frailty at an early stage and implementing intervention makes it possible to extend healthy life expectancy. Our previous research showed that frailty detection is possible with acceptable accuracy only using electricity power consumption data from smart meters. However, for real-world implementation, it is crucial to guarantee the robustness of the model performance. Moreover, implementing interventions after detecting frailty and ongoing monitoring is essential to extending healthy life expectancy. Given these points, we aim to guarantee the robustness of frailty detection model and verify the effectiveness of frailty detection model as a post-intervention monitoring tool. As a result of case study conducted in three regions, we demonstrated that the proposed frailty detection model has seasonal and regional robustness, showing acceptable performance in various seasons and regions. We also confirmed that it is possible to observe the elderly’s condition by tracking the outputs of the proposed model. Through our research, we showed the feasibility of frailty detection using smart meters and the possibility of a healthcare platform using smart meters.