Assessing Physical Rehabilitation Exercises using Graph Convolutional Network with Self-supervised regularization

Chen, Du; Depp, Colin A.; Nguyen, Truong Q.

doi:10.1109/embc46164.2021.9629569

Cited by 15 publications

(17 citation statements)

References 27 publications

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“…CNNs are designed to learn spatial hierarchies of features automatically and adaptively through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers [76]. The CNNs have been trained on large-scale datasets and on graphstructured data for motion analysis issue [35,77]. CNNs achieved outstanding accuracies in the computer vision field for human detection and pose estimation that is useful to compute position and orientation data of interesting joints [33,39,42,44], but also for activity monitoring [32,40,41,52] and movement evaluation [43].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…Human pose estimation is a field of computer vision that aims to predict the poses of human bodies by extracting joints from images and videos for motion analysis [65]. Contrarily to wearable sensors, AI-based human motion modeling enables commercial systems equipped with a camera and low-cost hardware, such as tablets and smartphones, to perform inexpensive and unobtrusive home-based monitoring in patients' daily life [33,35,36].…”

Section: B Ai-based Systems and Technologiesmentioning

confidence: 99%

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The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review

et al. 2023

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Artificial intelligence technologies are considered crucial in supporting a decentralized model of care in which therapeutic interventions are provided from a distance. In the last years, various approaches have been proposed to support remote monitoring and smart assistance in rehabilitation services. A comprehensive state-of-the-art of machine learning methods and applications is presented in this review. Following PRISMA guidelines, a systematic literature search strategy was led in PubMed, Scopus, and IEEE Xplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervised and unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologies have been identified as strategic applications to support remote and smart monitoring. The most addressed tasks by algorithms were activity recognition, movement classification, and clinical status prediction. Some authors evidenced drawbacks concerning the low generalizability of the results retrieved. Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitation services by providing broad access to sustained and high-quality therapy. Future efforts are needed to validate artificial intelligence technologies in specific clinical populations and evaluate results reliability in remote conditions and home-based settings. INDEX TERMS Digital therapeutics, e-health, remote monitoring, intelligent systems, deep learning, machine learning.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Section: B Ai-based Systems and Technologiesmentioning

confidence: 99%

The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review

et al. 2023

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“…Our work can be classified under AQA and SA, which involves the computer vision-based quantification of the quality of movements and actions. Works in AQA and SA have mainly been focused on domains like physiotherapy [6,19,25,33,36], Olympic sports [3,24,28,35,39,41], various types of skills [5,20,26,38]. However, workout form assessment, especially, in real-world conditions, has not received much attention.…”

Section: Related Workmentioning

confidence: 99%

Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment

Parmar¹,

Gharat²,

Rhodin³

2022

Preprint

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Maintaining proper form while exercising is important for preventing injuries and maximizing muscle mass gains. While fitness apps are becoming popular, they lack the functionality to detect errors in workout form. Detecting such errors naturally requires estimating users' body pose. However, off-the-shelf pose estimators struggle to perform well on the videos recorded in gym scenarios due to factors such as camera angles, occlusion from gym equipment, illumination, and clothing. To aggravate the problem, the errors to be detected in the workouts are very subtle. To that end, we propose to learn exercise-specific representations from unlabeled samples such that a small dataset annotated by experts suffices for supervised error detection. In particular, our domain knowledge-informed self-supervised approaches exploit the harmonic motion of the exercise actions, and capitalize on the large variances in camera angles, clothes, and illumination to learn powerful representations. To facilitate our self-supervised pretraining, and supervised finetuning, we curated a new exercise dataset, Fitness-AQA, comprising of three exercises: BackSquat, BarbellRow, and OverheadPress. It has been annotated by expert trainers for multiple crucial and typically occurring exercise errors. Experimental results show that our selfsupervised representations outperform off-the-shelf 2D-& 3D-pose estimators and several other baselines.

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“…Publicly available datasets comprised 17.8% of the total (13/73). The most commonly used public datasets were the UI-PRMD dataset [121] used in six publications [55], [56], [70], [75], [78], [122], and the KIMORE dataset [123] used in five publications [30], [40], [78], [80], [96]. Table 7 provides information about all publicly available datasets, including a reference to the link from which each one can be accessed.…”

Section: D: Datasets Availabilitymentioning

confidence: 99%

“…Such transformations can provide additional contextual information that is relevant to the target modality or take advantage of existing approaches optimized using this modality. The most frequently used technique in this category was remapping data into a graph-based representation [40], [55], [56], [75].…”

Section: ) Feature Extraction Engineering and Selectionmentioning

confidence: 99%

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.

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Assessing Physical Rehabilitation Exercises using Graph Convolutional Network with Self-supervised regularization

Cited by 15 publications

References 27 publications

The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review

The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review

Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

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