Online detection of compensatory strategies in human movement with supervised classification: a pilot study - Das, Neha; Endo, Satoshi; Patel, Sabrina; Krewer, Carmen; Hirche, Sandra (Frontiers in Neurorobotics, 2023)
Stroke survivors often compensate for the loss of motor function in their distal joints by altered use of more proximal joints and body segments. This can be detrimental to the rehabilitation process in the long-term as it hinders regaining normal function of impaired joints in the training.It is therefore imperative that such movements are indicated to the patients and their caregiver, so that the most effective exercise can be encouraged.Recent works have focused on supervised machine learning methods for compensation detection. However, existing methods for automatic detection of compensatory strategies suffer from numerous challenges. Since compensation strategies are varied and multi-faceted, and may depend on many factors including the person's capability and habits as well as the underlying task and intention, automatic detection of affected joints is a difficult task. Furthermore, previously proposed approaches for compensation detection have often required a large training dataset of motions with dense annotations by experts that indicate compensation location at each time-step of the recorded motion. This can place a severe burden on the annotator and makes the data collection process increasingly difficult.To mitigate this problem, we propose a novel approach that can, given a dataset of healthy and compensated motions without explicit annotations regarding location of the compensating joints, learn a linear classifier to discriminate between healthy and compensatory movements and exploit its weights to identify the compensating joints automatically. Our method exploits energybased features inspired by works in the domain of bio-mechanics that investigate generation of human movement. For compensating joint identification, we additionally impose a condition 1that all features must be independently calculated for each observable degree of freedom. Using a dataset of healthy and acted compensatory motions executed by physiotherapists, we show that a linear classifier trained on energy-based features can outperform deep learning-based architectures including MLPs and LSTM networks on online compensation detection tasks.We additionally demonstrate that this learned linear classifier is able to identify the joints that contribute to the compensation classification. Finally, we provide a measure of how much this contribution is by interpreting the product of the feature input and classifier weight.