Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 10; p. 2970
• Main Authors: Xia, Shenghao, Wung, Shu-Fen, Chen, Chang-Chun, Coompson, Jude Larbi Kwesi, Roveda, Janet, Liu, Jian
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.05.2024
• Subjects: Aged; Algorithms; Assisted living facilities; Avionics industry; Bayes Theorem; Bayesian inference; Blood pressure; data quality; Disease; Female; functional data analysis; Gaussian process; Heart beat; Heart Rate - physiology; Humans; Information management; Male; Measuring instruments; Missing data; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; Neural networks; Older people; precision care; Prospective Studies; Sensors; Time series; Variables; Wearable Electronic Devices; wearable sensors; United States; Finland; data quality; Gaussian process; precision care; functional data analysis; personalized patterns; wearable sensors; heart rate patterns; Bayesian inference
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24102970

The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects’ diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean.