Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/6/1732 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850280298831413248 |
|---|---|
| author | Karin Takahashi Yoshinobu Tanno Hitoshi Ueno |
| author_facet | Karin Takahashi Yoshinobu Tanno Hitoshi Ueno |
| author_sort | Karin Takahashi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4b877c8e7a5243718b7b4f20fee3f01a |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4b877c8e7a5243718b7b4f20fee3f01a2025-08-20T01:48:49ZengMDPI AGSensors1424-82202025-03-01256173210.3390/s25061732Identification of People in a Household Using Ballistocardiography Signals Through Deep LearningKarin Takahashi0Yoshinobu Tanno1Hitoshi Ueno2Faculty of Information Design, Tokyo Information Design Professional University, Edogawa-ku, Tokyo 132-0034, JapanFaculty of Information Design, Tokyo Information Design Professional University, Edogawa-ku, Tokyo 132-0034, JapanFaculty of Information Design, Tokyo Information Design Professional University, Edogawa-ku, Tokyo 132-0034, JapanBackground: 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.https://www.mdpi.com/1424-8220/25/6/1732biological signalpersonal identificationdeep learningmonitoring systempiezoelectric sensors |
| spellingShingle | Karin Takahashi Yoshinobu Tanno Hitoshi Ueno Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning Sensors biological signal personal identification deep learning monitoring system piezoelectric sensors |
| title | Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning |
| title_full | Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning |
| title_fullStr | Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning |
| title_full_unstemmed | Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning |
| title_short | Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning |
| title_sort | identification of people in a household using ballistocardiography signals through deep learning |
| topic | biological signal personal identification deep learning monitoring system piezoelectric sensors |
| url | https://www.mdpi.com/1424-8220/25/6/1732 |
| work_keys_str_mv | AT karintakahashi identificationofpeopleinahouseholdusingballistocardiographysignalsthroughdeeplearning AT yoshinobutanno identificationofpeopleinahouseholdusingballistocardiographysignalsthroughdeeplearning AT hitoshiueno identificationofpeopleinahouseholdusingballistocardiographysignalsthroughdeeplearning |