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)

Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review

9 Citaten (Scopus)

412 Downloads (Pure)

Samenvatting

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.

Originele taal-2	Engels
Artikelnummer	2250047
Aantal pagina's	11
Tijdschrift	International Journal of Neural Systems
Volume	32
Nummer van het tijdschrift	10
DOI's	https://doi.org/10.1142/S0129065722500472
Status	Gepubliceerd - 1 okt. 2022

Bibliografische nota

Publisher Copyright:
© World Scientific Publishing Company.

Financiering

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

Toegang tot document

10.1142/S0129065722500472

pdfDefinitieve gepubliceerde versie, 598 KBLicentie: TAVERNE

Andere bestanden en links

Link naar publicatie in Scopus

Citeer dit

@article{dff96d98737c423e8112edbb359d05ba,

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. ",

year = "2022",

month = oct,

day = "1",

doi = "10.1142/S0129065722500472",

language = "English",

volume = "32",

journal = "International Journal of Neural Systems",

issn = "0129-0657",

publisher = "World Scientific",

number = "10",

}

TY - JOUR

T1 - Development of an AI-Enabled System for Pain Monitoring Using Skin Conductance Sensoring in Socks

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.

KW - Mobile application

KW - pain measurement

KW - random forest prediction

KW - smart sock wearable

KW - Pain/diagnosis

KW - Artificial Intelligence

KW - Humans

KW - Adult

UR - https://www.scopus.com/pages/publications/85138032256

U2 - 10.1142/S0129065722500472

DO - 10.1142/S0129065722500472

M3 - Article

C2 - 36073508

AN - SCOPUS:85138032256

SN - 0129-0657

VL - 32

JO - International Journal of Neural Systems

JF - International Journal of Neural Systems

IS - 10

M1 - 2250047

ER -

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

Samenvatting

Bibliografische nota

Financiering

Toegang tot document

Andere bestanden en links

Vingerafdruk

Citeer dit