Development of an AI-Enabled System for Pain Monitoring Using Skin Conductance Sensoring in Socks

Helen Korving; Di Zhou; Huan Xiang; Paula Sterkenburg; Panos Markopoulos; Emilia Barakova

doi:10.1142/S0129065722500472

Development of an AI-Enabled System for Pain Monitoring Using Skin Conductance Sensoring in Socks

Helen Korving
, Di Zhou
, Huan Xiang
, Paula Sterkenburg
, Panos Markopoulos
, Emilia Barakova (Corresponding author)

Research output: Contribution to journal › Article › Academic › peer-review

8 Citations (Scopus)

353 Downloads (Pure)

Abstract

Background: Where self-report is unfeasible or observations are difficult, physiological estimates of pain are needed. Methods: Pain-data from 30 healthy adults were gathered to create a database of physiological pain responses. A model was then developed, to analyze pain-data and visualize the AI-estimated level of pain on a mobile app. Results: The initial low precision and F1-score of the pain classification algorithm were resolved by interpolating a percentage of similar data. Discussion: This system presents a novel approach to assess pain in noncommunicative people with the use of a sensor sock, AI predictor and mobile app. Performance analysis and the limitations of the AI algorithm are discussed.

Original language	English
Article number	2250047
Number of pages	11
Journal	International Journal of Neural Systems
Volume	32
Issue number	10
DOIs	https://doi.org/10.1142/S0129065722500472
Publication status	Published - 1 Oct 2022

Bibliographical note

Funding Information:
The authors would like to acknowledge the work of Ir. Bart van Dijk and Ir. Paul van Beek on programming the SID Pain App.

Funding

The authors would like to acknowledge the work of Ir. Bart van Dijk and Ir. Paul van Beek on programming the SID Pain App.

Keywords

Mobile application
pain measurement
random forest prediction
smart sock wearable
Pain/diagnosis
Artificial Intelligence
Humans
Adult

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10.1142/S0129065722500472

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Cite this

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title = "Development of an AI-Enabled System for Pain Monitoring Using Skin Conductance Sensoring in Socks",

abstract = "Background: Where self-report is unfeasible or observations are difficult, physiological estimates of pain are needed. Methods: Pain-data from 30 healthy adults were gathered to create a database of physiological pain responses. A model was then developed, to analyze pain-data and visualize the AI-estimated level of pain on a mobile app. Results: The initial low precision and F1-score of the pain classification algorithm were resolved by interpolating a percentage of similar data. Discussion: This system presents a novel approach to assess pain in noncommunicative people with the use of a sensor sock, AI predictor and mobile app. Performance analysis and the limitations of the AI algorithm are discussed.",

keywords = "Mobile application, pain measurement, random forest prediction, smart sock wearable, Pain/diagnosis, Artificial Intelligence, Humans, Adult",

author = "Helen Korving and Di Zhou and Huan Xiang and Paula Sterkenburg and Panos Markopoulos and Emilia Barakova",

note = "Funding Information: The authors would like to acknowledge the work of Ir. Bart van Dijk and Ir. Paul van Beek on programming the SID Pain App. ",

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AU - Korving, Helen

AU - Zhou, Di

AU - Xiang, Huan

AU - Sterkenburg, Paula

AU - Markopoulos, Panos

AU - Barakova, Emilia

N1 - Funding Information: The authors would like to acknowledge the work of Ir. Bart van Dijk and Ir. Paul van Beek on programming the SID Pain App.

PY - 2022/10/1

Y1 - 2022/10/1

N2 - Background: Where self-report is unfeasible or observations are difficult, physiological estimates of pain are needed. Methods: Pain-data from 30 healthy adults were gathered to create a database of physiological pain responses. A model was then developed, to analyze pain-data and visualize the AI-estimated level of pain on a mobile app. Results: The initial low precision and F1-score of the pain classification algorithm were resolved by interpolating a percentage of similar data. Discussion: This system presents a novel approach to assess pain in noncommunicative people with the use of a sensor sock, AI predictor and mobile app. Performance analysis and the limitations of the AI algorithm are discussed.

AB - Background: Where self-report is unfeasible or observations are difficult, physiological estimates of pain are needed. Methods: Pain-data from 30 healthy adults were gathered to create a database of physiological pain responses. A model was then developed, to analyze pain-data and visualize the AI-estimated level of pain on a mobile app. Results: The initial low precision and F1-score of the pain classification algorithm were resolved by interpolating a percentage of similar data. Discussion: This system presents a novel approach to assess pain in noncommunicative people with the use of a sensor sock, AI predictor and mobile app. Performance analysis and the limitations of the AI algorithm are discussed.

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KW - random forest prediction

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KW - Pain/diagnosis

KW - Artificial Intelligence

KW - Humans

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Development of an AI-Enabled System for Pain Monitoring Using Skin Conductance Sensoring in Socks

Abstract

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Keywords

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