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
JUFO-taso: 1
