An Edge Computing and Ambient Data Capture System for Clinical and Home Environments

• Type of Material: Article
• Authors: Pradyumma B Suresha, Emory University; Chaitra Hegde, Georgia Institute of Technology; Zifan Jiang, Emory University; Gari Clifford, Emory University
• Language: English
• Date: 2022-04-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2022 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/s22072511
• Title of Journal or Parent Work: SENSORS
• Volume: 22
• Issue: 7
• Grant/Funding Information: This research was supported by an Amazon Web Services Machine Learning Research Award, the Cox Foundation and the National Science Foundation, grant # 1822378 ‘Leveraging Heterogeneous Data Across International Borders in a Privacy Preserving Manner for Clinical Deep Learning’. Gari D. Clifford is also funded by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378 and National Institute of Environmental Health Sciences under grant # 2P30ES019776-05. The content is solely the responsibility of the authors and does not necessarily represent the official views of the authors’ sponsors and employers.
• Keywords: SLEEP; geolocation tracking; Engineering, Electrical & Electronic; Raspberry Pi; Physical Sciences; patient alarm; CIRCADIAN-RHYTHMS; ambient health monitoring; Chemistry, Analytical; illuminance; LIGHT; Chemistry; Engineering; PERFORMANCE; Instruments & Instrumentation; BODY AREA NETWORKS; STANDARDS; Science & Technology; bluetooth; edge computing; NOISE; privacy-preserving; Technology; ICU; MONITORING-SYSTEM
• Research Categories: Engineering, Biomedical

Abstract

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (1) Estimating occupancy and human activity phenotyping; (2) Medical equipment alarm classification; (3) Geolocation of humans in a built environment; (4) Ambient light logging; and (5) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3◦ C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.