Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
Institute of Physiotheraphy and Health Science, Academy of Physical Education in Katowice, Mikołowska 72a, 40-065 Katowice, Poland.
Sensors (Basel). 2021 Feb 12;21(4):1311. doi: 10.3390/s21041311.
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.
筋膜疗法是一种有效但疼痛的治疗方法。关于疼痛程度的信息对于理疗师调整治疗过程和避免潜在的组织损伤至关重要。由于自我报告的主观性,我们开发了一种用于自动评估理疗中与疼痛相关的反应的方法。基于多模态数据集,我们确定了特征向量,包括小波散射变换系数。AdaBoost 分类模型将反应分为三个级别(无疼痛、中度疼痛和重度疼痛)。由于患者的疼痛反应和疼痛耐受力不同,我们的调查采用了基于个体的方案。结果反映了患者对疼痛的个体感知。它们还表明,多类评估优于二进制识别。