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 toimittajat: Keskinarkaus, Anja; Yang, Ruijing; Fylakis, Angelos; Surat-E-Mostafa, Md.; Hautala, Arto; Hu, Yong; Peng, Jinye; Zhao, Guoying; Seppänen, Tapio; Karppinen, Jaro

Lehti tai sarja: Multimedia Tools and Applications

ISSN: 1380-7501

eISSN: 1573-7721

Julkaisuvuosi: 2022

Ilmestymispäivä: 29.12.2021

Volyymi: 81

Lehden numero: 4

Artikkelin sivunumerot: 5717-5742

Kustantaja: Springer

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1007/s11042-021-11761-8

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain 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-asiasanat: kipu; krooninen kipu; selkä; kivunhoito; monitorointi; koneoppiminen; signaalianalyysi; ilmeet; EEG; sykemittarit

Vapaat asiasanat: low back pain; machine learning; facial expression; audio analysis; heart rate; electroencephalography


Liittyvät organisaatiot


OKM-raportointi: Kyllä

Raportointivuosi: 2022

Alustava JUFO-taso: 1


Viimeisin päivitys 2022-20-09 klo 15:29