ArticleIEEE journal of translational engineering in health and medicine2025
Design and Evaluation of Volunteer User Trials of Unobtrusive Vital Signs Monitoring for Older People in Care Using Wi-Fi CSI Sensing.
Article in IEEE journal of translational engineering in health and medicine, 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
objectiveThis study describes the design and evaluation of volunteer user trials of an unobtrusive Wi-Fi Channel State Information (CSI) vital sign sensing system in older participants aged 60 years and older in different home environments. METHODS AND PROCEDURES: In terms of experiment design, the implementation of user-centric sensor placement and integration informed consent with various experimental elements in the design of experiments of older people. The implemented signal processing algorithm, which extracts vital signs from the Wi-Fi CSI signal to obtain respiration and heart rate measurements, employs wavelet filtering techniques. For selecting of vital sign signals from the 52 CSI subcarriers, the Principal Component Sample Entropy (PC-SampEn) was implemented to capture the information most relevant to vital signs.
resultsTwo cardiorespiratory vital sign measurements were validated against wearable ground-truth devices, a respiratory belt and a photoplethysmogram (PPG). The results demonstrated an expected decrease in accuracy and measurement agreement in uncontrolled home environments.
conclusionAlthough respiratory rate measurements have demonstrated promising accuracy and agreement in uncontrolled environments, heart rate measurements observed high variability in these scenarios due to challenging signal extraction. Further experiments must be conducted to address the limitation in sample size and the technical challenges in heart rate signal extraction to improve accuracy. Clinical and Translational Impact: This study provides a design of unobtrusive care technology for vital sign sensing for older adults, demonstrated and evaluated in the context of in-home monitoring for healthcare.
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