Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning - Karin Takahashi; Yoshinobu Tanno; Hitoshi Ueno (Sensors)
Background: Various sensor technologies have been developed to monitor the health of older adults; however, most of them require attachment to the skin. This study aimed to develop a health monitoring system, using a non-adhesive, non-invasive polyvinylidene difluoride piezoelectric sensor, with the patient being able to lead a normal daily life without being conscious of the sensor. The vibration signal from the human body surface obtained by the piezoelectric sensor, which is a ballistocardiography signal, contains information on the person’s heart and respiratory rates. We propose a method that enables individual identification based on the characteristics of the frequency components of the signal. Methods: Signals from ten subjects were acquired and a neural network was constructed, trained, and tested using 252 cases to identify five individuals, based on assuming the number of people in a household. Results: The classification probability and accuracy rate were obtained for all 252 cases, and good classification rates were obtained in almost all cases. Conclusions: Although it will be necessary to consider daily changes in such signals in the future, the system had good identification accuracy when five individuals were identified.