Classification of Free-Living Body Posture with ECG Patch Accelerometers: Application to the Multicenter AIDS Cohort Study

Lacey H. Etzkorn (), Amir S. Heravi, Nicolas D. Knuth, Katherine C. Wu, Wendy S. Post, Jacek K. Urbanek and Ciprian M. Crainiceanu
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Lacey H. Etzkorn: Johns Hopkins University
Amir S. Heravi: Johns Hopkins University
Nicolas D. Knuth: Towson University
Katherine C. Wu: Johns Hopkins University
Wendy S. Post: Johns Hopkins University
Jacek K. Urbanek: Johns Hopkins University
Ciprian M. Crainiceanu: Johns Hopkins University

Statistics in Biosciences, 2024, vol. 16, issue 1, No 2, 25-44

Abstract: Abstract As health studies increasingly monitor free-living heart performance via ECG patches with accelerometers, researchers will seek to investigate cardio-electrical responses to physical activity and sedentary behavior, increasing demand for fast, scalable methods to process accelerometer data. We extend a posture classification algorithm for accelerometers in ECG patches when researchers do not have ground-truth labels or other reference measurements (i.e., upright measurement). Men living with and without HIV in the Multicenter AIDS Cohort study wore the Zio XT® for up to 2 weeks (n = 1250). Our novel extensions for posture classification include (1) estimation of an upright posture for each individual without a reference upright measurement; (2) correction of the upright estimate for device removal and re-positioning using novel spherical change point detection; and (3) classification of upright and recumbent periods using a clustering and voting process rather than a simple inclination threshold used in other algorithms. As no posture labels exist in the free-living environment, we perform numerous sensitivity analyses and evaluate the algorithm against labeled data from the Towson Accelerometer Study, where participants wore accelerometers at the waist. On average, 87.1% of participants were recumbent at 4 a.m. and 15.5% were recumbent at 1 p.m. Participants were recumbent 54 min longer on weekends compared to weekdays. Performance was good in comparison to labeled data in a separate, controlled setting (accuracy = 96.0%, sensitivity = 97.5%, specificity = 95.9%). Posture may be classified in the free-living environment from accelerometers in ECG patches even without measuring a standard upright position. Furthermore, algorithms that fail to account for individuals who rotate and re-attach the accelerometer may fail in the free-living environment.

Keywords: Actigraphy; Electrocardiogram; Change point; Clustering; Sedentary behavior; Static activity (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12561-023-09377-7

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