The annotation of domain experts is important for some medical applications where the objective ground truth is ambiguous to define, e.g., the rehabilitation for some chronic diseases, and the prescreening of some musculoskeletal abnormalities without further medical examinations. However, improper uses of the annotations may hinder developing reliable models. On one hand, forcing the use of a single ground truth generated from multiple annotations is less informative for the modeling. On the other hand, feeding the model with all the annotations without proper regularization is noisy given existing disagreements. For such issues, we propose a novel Learning to Agreement (Learn2Agree) framework to tackle the challenge of learning from multiple annotators without objective ground truth. The framework has two streams, with one stream fitting with the multiple annotators and the other stream learning agreement information between annotators. In particular, the agreement learning stream produces regularization information to the classifier stream, tuning its decision to be better in line with the agreement between annotators. The proposed method can be easily added to existing backbones, with experiments on two medical datasets showed better agreement levels with annotators.

For people with chronic pain (CP), the assessment of protective behavior during physical functioning is essential to understand their subjective pain-related experiences (e.g., fear and anxiety toward pain and injury) and how they deal with such experiences (avoidance or reliance on specific body joints), with the ultimate goal of guiding intervention. Advances in deep learning (DL) can enable the development of such intervention. Using the EmoPain MoCap dataset, we investigate how attention-based DL architectures can be used to improve the detection of protective behavior by capturing the most informative biomechanical cues characterizing specific movements and the strategies used to execute them to cope with pain-related experience. We propose an end-to-end neural network architecture based on attention mechanism, named BodyAttentionNet (BANet). BANet is designed to learn temporal and body-joint regions that are informative to the detection of protective behavior. The approach can consider the variety of ways people execute one movement (including healthy people) and it is independent of the type of movement analyzed. We also explore variants of this architecture to understand the contribution of both temporal and bodily attention mechanisms. Through extensive experiments with other state-of-the-art machine learning techniques used with motion capture data, we show a statistically significant improvement achieved by combining the two attention mechanisms. In addition, the BANet architecture requires a much lower number of parameters than the state-of-the-art ones for comparable if not higher performances.

In chronic pain physical rehabilitation, physiotherapists adapt movement to current performance of patients especially based on the expression of protective behavior, gradually exposing them to feared but harmless and essential everyday movements. As physical rehabilitation moves outside the clinic, physical rehabilitation technology needs to automatically detect such behaviors so as to provide similar personalized support. In this paper, we investigate the use of a Long Short-Term Memory (LSTM) network, which we call Protect-LSTM, to detect events of protective behavior, based on motion capture and electromyography data of healthy people and people with chronic low back pain engaged in five everyday movements. Differently from previous work on the same dataset, we aim to continuously detect protective behavior within a movement rather than overall estimate the presence of such behavior. The Protect-LSTM reaches best average F1 score of 0.815 with leave-one-subject-out (LOSO) validation, using low level features, better than other algorithms. Performances increase for some movements when modelled separately (mean F1 scores: bending=0.77, standing on one leg=0.81, sit-to-stand=0.72, stand-to-sit=0.83, reaching forward=0.67). These results reach excellent level of agreement with the average ratings of physiotherapists. As such, the results show clear potential for in-home technology supported affect-based personalized physical rehabilitation.