research-article

Painometry: wearable and objective quantification system for acute postoperative pain

MobiSys '20: Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services

Pages 419 - 433

https://doi.org/10.1145/3386901.3389022

Published: 15 June 2020 Publication History

Abstract

Over 50 million people undergo surgeries each year in the United States, with over 70% of them filling opioid prescriptions within one week of the surgery. Due to the highly addictive nature of these opiates, a post-surgical window is a crucial time for pain management to ensure accurate prescription of opioids. Drug prescription nowadays relies primarily on self-reported pain levels to determine the frequency and dosage of pain drug. Patient pain self-reports are, however, influenced by subjective pain tolerance, memories of past painful episodes, current context, and the patient's integrity in reporting their pain level. Therefore, objective measures of pain are needed to better inform pain management.

This paper explores a wearable system, named Painometry, which objectively quantifies users' pain perception based-on multiple physiological signals and facial expressions of pain. We propose a sensing technique, called sweep impedance profiling (SIP), to capture the movement of the facial muscle corrugator supercilii, one of the important physiological expressions of pain. We deploy SIP together with other biosignals, including electroencephalography (EEG), photoplethysmogram (PPG), and galvanic skin response (GSR) for pain quantification.

From the anatomical and physiological correlations of pain with these signals, we designed Painometry, a multimodality sensing system, which can accurately quantify different levels of pain safely. We prototyped Painometry by building a custom hardware, firmware, and associated software. Our evaluations use the prototype on 23 subjects, which corresponds to 8832 data points from 276 minutes of an IRB-approved experimental pain-inducing protocol. Using leave-one-out cross-validation to estimate performance on unseen data shows 89.5% and 76.7% accuracy of quantification under 3 and 4 pain states, respectively.

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Cited By

Wipperman MPogoncheff GMateo KWu XChen YLevy OAvbersek ADeterding RHamon SVu TAlaj RHarari O(2022)A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteersPLOS Digital Health10.1371/journal.pdig.00000611:6(e0000061)Online publication date: 30-Jun-2022
https://doi.org/10.1371/journal.pdig.0000061
Pan SNguyen P(2020)Opportunities in the Cross-Scale Collaborative Human Sensing of 'Developing' Device-Free and Wearable SystemsProceedings of the 2nd ACM Workshop on Device-Free Human Sensing10.1145/3427772.3429394(16-21)Online publication date: 15-Nov-2020
https://dl.acm.org/doi/10.1145/3427772.3429394

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Painometry: wearable and objective quantification system for acute postoperative pain

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Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio

As America's opioid crisis has become an "epidemic of epidemics," Ohio has been identified as one of the high burden states regarding fentanyl-related overdose mortality. This study aims to examine changes in the availability of fentanyl, fentanyl ...
DOVE: Shoulder-based Opioid Overdose Detection and Reversal Device
CHASE '23: Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies

Naloxone is a life-saving drug capable of reversing a fatal opioid overdose. Although this drug has existed for over 50 years, opioid overdose-related deaths have consistently risen and surpassed 120,000 globally in 2021. Opioids induce respiratory ...

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cover image ACM Conferences

MobiSys '20: Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services

June 2020

496 pages

ISBN:9781450379540

DOI:10.1145/3386901

General Chairs:
Eyal de Lara
University of Toronto
,
Iqbal Mohomed
Samsung AI Centre Toronto
,
Program Chairs:
Jason Nieh
Columbia University
,
Elizabeth M. Belding
UC Santa Barbara

Copyright © 2020 ACM.

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 June 2020

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National Science Foundation

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MobiSys '20

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SIGMOBILE

MobiSys '20: The 18th Annual International Conference on Mobile Systems, Applications, and Services

June 15 - 19, 2020

Ontario, Toronto, Canada

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Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

Wipperman MPogoncheff GMateo KWu XChen YLevy OAvbersek ADeterding RHamon SVu TAlaj RHarari O(2022)A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteersPLOS Digital Health10.1371/journal.pdig.00000611:6(e0000061)Online publication date: 30-Jun-2022
https://doi.org/10.1371/journal.pdig.0000061
Pan SNguyen P(2020)Opportunities in the Cross-Scale Collaborative Human Sensing of 'Developing' Device-Free and Wearable SystemsProceedings of the 2nd ACM Workshop on Device-Free Human Sensing10.1145/3427772.3429394(16-21)Online publication date: 15-Nov-2020
https://dl.acm.org/doi/10.1145/3427772.3429394

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