Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 18 February 2022 / Approved: 21 February 2022 / Online: 21 February 2022 (05:06:48 CET)
Version 2 : Received: 16 March 2022 / Approved: 16 March 2022 / Online: 16 March 2022 (05:28:32 CET)

A peer-reviewed article of this Preprint also exists.

Suresha, P.B.; Hegde, C.; Jiang, Z.; Clifford, G.D. An Edge Computing and Ambient Data Capture System for Clinical and Home Environments. Sensors 2022, 22, 2511. Suresha, P.B.; Hegde, C.; Jiang, Z.; Clifford, G.D. An Edge Computing and Ambient Data Capture System for Clinical and Home Environments. Sensors 2022, 22, 2511.

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: (a) Estimating occupancy and human activity phenotyping; (b) Medical equipment alarm classification; (c) Geolocation of humans in a built environment; (d) Ambient light logging; and (e) 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.

Keywords

Raspberry Pi; Edge Computing; Ambient Health Monitoring; Privacy-preserving; Bluetooth; Geolocation Tracking; Patient Alarm; Illuminance

Subject

Engineering, Electrical and Electronic Engineering

Comments (1)

Comment 1
Received: 16 March 2022
Commenter: Pradyumna Byappanahalli Suresha
Commenter's Conflict of Interests: Author
Comment: We received major review from the reviewers at MDPI Sensors journal. We made numerous changes to adhere to the recommendations made by the reviewers. The current version is thus modified signficantly.
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