ArticleSensors (Basel, Switzerland)2025
Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
backgroundVarious 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.
methodsSignals 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.
resultsThe classification probability and accuracy rate were obtained for all 252 cases, and good classification rates were obtained in almost all cases.
conclusionsAlthough 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.
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