Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform

被引：11

作者：

Badura, Aleksandra ^{[1
]}

Maslowska, Aleksandra ^{[2
]}

Mysliwiec, Andrzej ^{[2
]}

Pietka, Ewa ^{[1
]}

机构：

[1] Silesian Tech Univ, Fac Biomed Engn, Roosevelta 40, PL-41800 Zabrze, Poland

[2] Acad Phys Educ Katowice, Inst Physiotheraphy & Hlth Sci, Mikolowska 72a, PL-40065 Katowice, Poland

来源：

SENSORS | 2021年 / 21卷 / 04期

关键词：

pain assessment; pain monitoring; physiotherapy; FACIAL EXPRESSION;

D O I：

10.3390/s21041311

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Fascial therapy is an effective, yet painful, procedure. Information about pain level is essential for the physiotherapist to adjust the therapy course and avoid potential tissue damage. We have developed a method for automatic pain-related reaction assessment in physiotherapy due to the subjectivity of a self-report. Based on a multimodal data set, we determine the feature vector, including wavelet scattering transforms coefficients. The AdaBoost classification model distinguishes three levels of reaction (no-pain, moderate pain, and severe pain). Because patients vary in pain reactions and pain resistance, our survey assumes a subject-dependent protocol. The results reflect an individual perception of pain in patients. They also show that multiclass evaluation outperforms the binary recognition.

引用

页码：1 / 14

页数：13

共 42 条

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