Takayuki Fujita

The incidence of falls in hospital facilities is high and can lead to a decrease in patients' quality of life and an increase in medical expenses. Therefore, the development of a system that can predict getting-up from a bed is necessary. This study proposes a get-up detecting sensor using 6-axis inertial sensor. This system can detect getting-up from a bed in real-time using machine learning with Edge AI. To evaluate the basic performance of the proposed system, a protocol was applied for four subjects, and data were collected. Alert accuracy rate and false alert rate were used as evaluation metrics, and a model was built using data from three of the subjects and evaluated with the remaining subject, which was repeated for all four subjects. For high-risk bed-leaving behavior, medium-risk pre-bed-leaving behavior, and low-risk get-up behavior, the alert accuracy rate (i.e., Recall) was 89.4%, 97.5%, and 86.3%, respectively, and the false alert rate (1-Precision) was 6.3%, 10.2%, and 0.0%, respectively. This confirmed the possibility of predicting rising behavior with high accuracy. Furthermore, as a proof of concept, a real-time get-up detection system was developed, and its practicality was demonstrated. Future challenges include reevaluating feature extraction and evaluating the performance of the proposed system with a diverse range of subjects of different ages, genders, and health statuses.