A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Pain fingerprinting using multimodal sensing : pilot study (2022)


Keskinarkaus, A., Yang, R., Fylakis, A., Surat-E-Mostafa, Md., Hautala, A., Hu, Y., Peng, J., Zhao, G., Seppänen, T., & Karppinen, J. (2022). Pain fingerprinting using multimodal sensing : pilot study. Multimedia Tools and Applications, 81(4), 5717-5742. https://doi.org/10.1007/s11042-021-11761-8


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatKeskinarkaus, Anja; Yang, Ruijing; Fylakis, Angelos; Surat-E-Mostafa, Md.; Hautala, Arto; Hu, Yong; Peng, Jinye; Zhao, Guoying; Seppänen, Tapio; Karppinen, Jaro

Lehti tai sarjaMultimedia Tools and Applications

ISSN1380-7501

eISSN1573-7721

Julkaisuvuosi2022

Ilmestymispäivä29.12.2021

Volyymi81

Lehden numero4

Artikkelin sivunumerot5717-5742

KustantajaSpringer

JulkaisumaaYhdysvallat (USA)

Julkaisun kielienglanti

DOIhttps://doi.org/10.1007/s11042-021-11761-8

Julkaisun avoin saatavuusAvoimesti saatavilla

Julkaisukanavan avoin saatavuusOsittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX)https://jyx.jyu.fi/handle/123456789/79331


Tiivistelmä

Pain is a complex phenomenon, the experience of which varies widely across individuals. At worst, chronic pain can lead to anxiety and depression. Cost-effective strategies are urgently needed to improve the treatment of pain, and thus we propose a novel home-based pain measurement system for the longitudinal monitoring of pain experience and variation in different patients with chronic low back pain. The autonomous nervous system and audio-visual features are analyzed from heart rate signals, voice characteristics and facial expressions using a unique measurement protocol. Self-reporting is utilized for the follow-up of changes in pain intensity, induced by well-designed physical maneuvers, and for studying the consecutive trends in pain. We describe the study protocol, including hospital measurements and questionnaires and the implementation of the home measurement devices. We also present different methods for analyzing the multimodal data: electroencephalography, audio, video and heart rate. Our intention is to provide new insights using technical methodologies that will be beneficial in the future not only for patients with low back pain but also patients suffering from any chronic pain.


YSO-asiasanatkipukrooninen kipuselkäkivunhoitomonitorointikoneoppiminensignaalianalyysiilmeetEEGsykemittarit

Vapaat asiasanatlow back pain; machine learning; facial expression; audio analysis; heart rate; electroencephalography


Liittyvät organisaatiot


OKM-raportointiKyllä

Raportointivuosi2022

JUFO-taso1


Viimeisin päivitys 2024-03-04 klo 19:45