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

Abstract

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.

A systematic review of neurophysiological sensing for the assessment of acute pain

“…However, this high sensitivity to sympathetic function makes the discrimination between pain and emotional states difficult to accomplish 31 ; this is a possible cause of false positives in pain detection 29 . EDA has exhibited promising results when used in isolation 29 , 31 , 32 , 42 , 45 and has showed better results when compared with other sensors, such as, sEMG and ECG 35 , 36 , and RESP, BVP, and EGM 98 .…”

“…For instance, in guiding the intensity of physical rehabilitation to identify the efficacy of treatment and to decrease the risk of re-injury, as well as in helping to design programs tailored to the specific pain sensitivity of each patient 156 . In this regard, Badura et al 98 designed a study to monitor pain in patients during fascial therapy, with the intention to use it as real-time feedback on the intensity of the therapy, to avoid any tissue damage, and to improve therapy outcomes. Another opportunity to the use of neurophysiological indicators of pain is guiding audiologists in finding the most suitable stimulation level for each patient in cochlear implants.…”

Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol

“…13 24 Specifically, Johnson et al 25 showed the feasibility of developing novel methods to assess pain by collecting physiological signals with wearable devices on 27 patients with sickle cell disease in a hospital setting using machine learning classifiers and regressors. In another work, Badura et al 26 applied the same approach in a physiotherapy setting, monitoring 35 patients who rated their pain during a session of fascial therapy. In addition, our group developed an automatic dichotomous classifier for pain assessment in oncological patients in a previous study.…”

Automatic assessment of pain based on deep learning methods: A systematic review

“…Effective pain assessment is essential for early diagnosis, disease progression monitoring, and evaluation of treatment efficacy, especially in managing chronic pain [3]. Additionally, adjusting pain intensity is crucial in therapy approaches like myofascial therapy, where a practitioner, such as a physiotherapist, externally induces the pain, and understanding the patient's pain level is vital [4]. Pain evaluation is crucial yet challenging for healthcare professionals [5], especially when dealing with patients who cannot communicate verbally.…”