Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning

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.

Authors

Publication Details

Journal
Sensors
Published
2025-03-11
DOI
https://doi.org/10.3390/s25061732
Citations
1
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
0.67
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article

Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning

Karin Takahashi, Hitoshi Ueno, Yoshinobu Tanno
1 citations
Sensors
Non-Invasive Vital Sign Monitoring
0.67
article

Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning

Karin Takahashi, Hitoshi Ueno, Yoshinobu Tanno
article en
1 citations

Abstract

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.

SensorsVol. 25(6)
Good health and well-being
Openalex Percentile: Top 34%
Non-Invasive Vital Sign Monitoring
0.67
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Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning — Karin Takahashi, Hitoshi Ueno, et al. · Sensors (2025) | TGRS Research Map | TGRS