Automatic Recognition of Low-Back Chronic Pain Level and Protective Movement Behaviour using Physical and Muscle Activity Information

Automatic recognition of low-back chronic pain and movement behaviour in humans could be a useful technology in health monitoring and providing effective rehabilitation advice. Physical and muscle activity information can be used in automating this process in combination with machine learning and feature engineering methods. This paper presents a method for automatic recognition of chronic pain and movement behaviour using our recently proposed 'Active Data Representation' (ADR) method, and applies it to two tasks of the EmoPain 2020 Challenge using physical and muscle activity features. The ADR method is used for the transformation of the physical and muscle activity features for the classification tasks. Our results show that ADR outperforms the LSTM challenge baseline model in terms of Matthews correlation coefficient (0.43) and F score (61.21) for the recognition of chronic pain and movement behaviour respectively in hold-out validation settings. Although a decrease in performance is observed on the test dataset, ADR still outperforms the challenge baseline for the recognition of chronic pain and movement behaviour tasks.