@inproceedings{Marusic2024I,
	abstract = {Studies indicate that physical rehabilitation exercises rec-
ommended by healthcare professionals can enhance physical function,
improve quality of life, and promote independence for physically dis-
abled individuals. In response to the lack of immediate expert feedback
on performed actions, developing an automated system for monitoring
such actions and providing feedback is very much needed. In this work,
we focus on skeleton-based exercise assessment, which uses skeleton data
to evaluate human motion and provide a score on how well a patient
performed a movement. There are several approaches to this issue, with
Spatio Temporal Graph Convolutional Networks (GCN) being among
the most recent. GCNs model skeleton data as graphs and utilize tem-
poral and spatial convolutions to capture relationships between joints
more effectively than previous methods. In this research, we propose a
new Transformer based model, PhysioFormer. It is inspired by Skate-
Former method for human action recognition, with enhanced structure
to fit the task of physical rehabilitation assessment. The model leverages
skeletal-temporal self-attention across different groups based on relations
between joints. The evaluation is done on the KIMORE, UI-PRMD, and
KERAAL datasets, benchmark datasets that provide skeleton data cap-
tured by Kinect motion system. Our model is surpassing state-of-the-art
methods significantly.},
	author = {Marusic, Aleksa and Nguyen, Sao Mai and Tapus, Adriana},
	booktitle = {ICSR},
	date-added = {2024-12-30 01:01:02 +0100},
	date-modified = {2024-12-30 01:01:02 +0100},
	keywords = {robot coach, humanoid robot, physical rehabilitation, motion assessment, human body movement analysis},
	read = {1},
	title = {PhysioFormer: A Spatio-Temporal Transformer for Physical Rehabilitation Assessmen},
	year = {2024},
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