Pain Monitoring

The analysis of pain sensation is of high importance in numerous medical applications. For example, in physiotherapeutic treatments, pain, or especially the pain threshold, can not only determine the course or result of the treatment, but can also influence the structure and composition of the exercises from the very beginning. The quantitative assessment of one's own pain is traditionally based on a self-assessment with questionnaires. However, this method is not suitable for patients who are not able to communicate their pain (objectively).

In cooperation with Prof. Kerstin Lüdtke (Physiotherapy, University of Lübeck) we are developing a learning-based pattern recognition platform that will automatically determine the current level of pain based on data from multiple, portable sensors. The data acquisition is performed with multiple devices. Pain is induced by heat using a thermode, CHEPS (Contact Heat-Evoked Potential Stimulator), on the forearm of the non-dominant arm of the study participants. During the experiment, participants are asked to continuously report their perceived pain level using a CoVAS (Computerised Visual Analogue Scale). In order to record the physical response of people in pain, two wearables (Empatica E4 and RespiBan) record different physiological characteristics (such as BVP, EDA, EMG, ECG) during the test. The data from the wearables, CoVAS and Thermode are synchronized and adjusted for sampling rate.

For the classification of pain vs. no pain (i.e. for a 2-class problem) we currently use a random forest procedure based on manually defined characteristics. Additionally, we evaluate different deep learning approaches for automatic learning based on the raw data. In the first pilot study with a data set of 10 healthy volunteers and a manually defined feature space (feature engineering) we achieved a classification rate of 76% for this 2-class problem. In the coming months we will significantly increase the volume of available training data, so that a significant improvement in the robustness of the classifier can be expected and additional pain classes can be added. An important aspect of our research in this area is to develop such machine learning methods for solving the problem, which will provide us with new insights into the physiological and behavioral markers of pain.

Third party funded projects and publications

BMBF-Projekt: PainMonit -  Multimodal Platform for Pain Monitoring in Physiotherapy. Duration: 01.01.2019 - 31.12.2021.

Philip Gouverneur, Frédéric Li, Tibor Szikszay, Waclaw Adamczyk, Kerstin Lüdtke and Marcin Grzegorzek, Classification of Heat-induced Pain using Physiological Signals, International Conference Information Technology in Biomedicine 2020