The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities
Open accessAbstract
Physical rehabilitation techniques during the treatment of clinical pathology are one of the most challenging areas for the medical structure, patients, and families. In large and continental countries, remote monitoring of this treatment is essential. However, equipment and medical follow-up during exercises still have high costs. With the improvement of computer vision and machine learning techniques, some computational, less expensive alternatives have been proposed in the literature. However, monitoring patients during physical rehabilitation exercises with the help of artificial intelligence by a health professional, especially from the capture of visual signals, is still a challenge and poorly explored in the scientific-technological literature. This work aims to propose a new methodology based on computer vision and machine learning for remote tracking of the body joints of patients during physiotherapy rehabilitation exercises. As a new contribution, this work presents a modular neural network architecture composed of two modules: one for detecting physical exercises and another for measuring how much is correct. Another contribution is a strategy for expanding databases, considering that generic databases for this type of exercise are rare on the internet. The results showed that both modules obtained more than 90% of accuracy in recognition and their respective validation.
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“Furthermore, computer vision and machine learning can accurately monitor home exercise movements [20].”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [20]
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Cites
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Cited by
Lindbäck, Y., Schröder, K., Engström, T., Valeskog, K., & Sonesson, S. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07106-w · Cited by 22
“Additionally, computer vision and machine learning can accurately monitor home exercise movements [20].”
Metadata
- Authors
- Francisco, J. A., & Rodrigues, P. S.
- Affiliations
- Centro Universitário FEI (BR)
- Computer Science Department, Centro Universitário FEI, São Bernardo do Campo, São Paulo, Brazil
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2023
- Volume
- 31
- Pages
- 2174-2183
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W4310639163
- Cited by
- 32
- References
- 21
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 8 · 2023: 3
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- physical rehabilitation, remote patient monitoring, exercise recognition, joint tracking, modular neural networks, physiotherapy exercises, low-cost rehabilitation
- MeSH terms
- Artificial Intelligence, Computers, Exercise Therapy, Humans, Neural Networks, Computer, Physical Therapy Modalities
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- Francisco, J. A., & Rodrigues, P. S. (2023). Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/tnsre.2022.3226459
