  Physical rehabilitation programs frequently begin with a brief stay in the
hospital and continue with home-based rehabilitation. Lack of feedback on
exercise correctness is a significant issue in home-based rehabilitation.
Automated movement quality assessment (MQA) using skeletal movement data
(hereafter referred to as skeletal data) collected via depth imaging devices
can assist with home-based rehabilitation by providing the necessary
quantitative feedback. This paper aims to use recent advances in deep learning
to address the problem of MQA. Movement quality score generation is an
essential component of MQA. We propose three novel skeletal data augmentation
schemes. We show that using the proposed augmentations for generating movement
quality scores result in significant performance boosts over existing methods.
Finally, we propose a novel transformer based architecture for MQA. Four novel
feature extractors are proposed and studied that allow the transformer network
to operate on skeletal data. We show that adding the attention mechanism in the
design of the proposed feature extractor allows the transformer network to pay
attention to specific body parts that make a significant contribution towards
executing a movement. We report an improvement in movement quality score
prediction of 12% on UI-PRMD dataset and 21% on KIMORE dataset compared to the
existing methods.
