The application of neural network approaches for physical rehabilitation

Research Article
Open access

The application of neural network approaches for physical rehabilitation

Duoqi Liu 1*
  • 1 Shanghai SMIC Private School, No. 169, Qing-Tong Road Pudong New Area, Shanghai, China 201203    
  • *corresponding author lhongjun.e08sh2@ceibs.edu
ACE Vol.4
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-55-3
ISBN (Online): 978-1-915371-56-0

Abstract

Physical rehabilitation is essential for a large number of patients around the world to recover from their disabilities. However, conventional methods of rehabilitation were expensive and difficult for majority of patients to access. This paper presents studies with neural network-based approaches that solve this problem are reviewed in this paper. The methods are divided into three main categories: stroke rehabilitation, injury rehabilitation and other rehabilitations. The common methods reviewed are Convolutional Neural Network (CNN), Support Vector Machine (SVM) and Recurrent Neural Network (RNN). There are various rehabilitation datasets reviewed in the paper, which all included pictures and videos of both patients and healthy people preforming a series of movements. The sensors used in experiments to capture patients’ movements are also summarized in this paper. The paper reviewed few algorithms able to model a 3D human skeleton based on the data collected by sensors. Evaluation metrics reviewed includes Discrete Movement Score, rule-based and template-based scoring methods. K-Nearest Neighbor (KNN) and Dynamic Time Warping (DTW) distance function were commonly used in template-based evaluation methods. The results in researches reviewed indicate that neural network-based rehabilitation methods are able to satisfy most demands, and improved efficiency, making it affordable and accessible to more patients.

Keywords:

Physical rehabilitation, Neural network, Machine learning, Movement evaluation method.

Liu,D. (2023). The application of neural network approaches for physical rehabilitation. Applied and Computational Engineering,4,279-283.
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References

[1]. Liao, Y., Vakanski, A., Xian, M., Paul, D., & Baker, R. (2020). A review of computational approaches for evaluation of rehabilitation exercises (Computers in biology and medicine) 119103687

[2]. Zhi Y X and Lukasik M and Li M H and Dolatabadi E and Wang R H and Taati B 2017 Automatic detection of compensation during robotic stroke rehabilitation therapy (IEEE journal of translational engineering in health and medicine) 6 1-7

[3]. Kidziński Ł and Yang B and Hicks J L and Rajagopal A and Delp S L and Schwartz M H 2020 Deep neural networks enable quantitative movement analysis using single-camera videos (Nature communications) 11(1) 1-10

[4]. Escalona F and Martinez-Martin E and Cruz E and Cazorla M and Gomez-Donoso F 2020 EVA: EVAluating at-home rehabilitation exercises using augmented reality and low-cost sensors (Virtual Reality) 24(4) 567-581

[5]. Machlin S R and Chevan J and Yu W W and Zodet M W 2011 Determinants of utilization and expenditures for episodes of ambulatory physical therapy among adults (Physical therapy) 91(7) 1018-1029

[6]. Wang S C 2003 Interdisciplinary computing in Java programming (Springer Science & Business Media) Vol. 743

[7]. Baptista R and Ghorbel E and Moissenet F and Aouada D and Douchet A and André M and Bouilland S 2019 Home self-training: Visual feedback for assisting physical activity for stroke survivors (Computer methods and programs in biomedicine) 176 111-120

[8]. Nascimento L M S D and Bonfati L V and Freitas M L B and Mendes Junior J J A and Siqueira H V and Stevan S L 2020 Sensors and systems for physical rehabilitation and health monitoring—A review (Sensors) 20(15) 4063

[9]. Turner C R and Fuggetta A and Lavazza L and Wolf A L and 1999 A conceptual basis for feature engineering (Journal of Systems and Software) 49(1) 3-15

[10]. Tao L and Paiement A and Damen D and Mirmehdi M and Hannuna S and Camplani M and Craddock I 2016 A comparative study of pose representation and dynamics modelling for online motion quality assessment (Computer vision and image understanding) 148 136-152

[11]. Li Y and Chai X and Chen X 2018 ScoringNet: Learning key fragment for action quality assessment with ranking loss in skilled sports (Asian Conference on Computer Vision) pp. 149-164

[12]. Liao Y and Vakanski A and Xian M 2020 A deep learning framework for assessing physical rehabilitation exercises (IEEE Transactions on Neural Systems and Rehabilitation Engineering) 28(2) 468-477

[13]. Bruce X B and Liu Y and Chan K C and Yang Q and Wang X 2021 Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression (Pattern Recognition) 119 108095

[14]. Liang F Y and Zhong C H and Zhao X and Castro D L and Chen B and Gao F and Liao W H 2018 Online adaptive and lstm-based trajectory generation of lower limb exoskeletons for stroke rehabilitation (2018 IEEE International Conference on Robotics and Biomimetics) pp. 27-32

[15]. Qiu Y et al. 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control 72 103323

Cite this article

Liu,D. (2023). The application of neural network approaches for physical rehabilitation. Applied and Computational Engineering,4,279-283.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.4
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Liao, Y., Vakanski, A., Xian, M., Paul, D., & Baker, R. (2020). A review of computational approaches for evaluation of rehabilitation exercises (Computers in biology and medicine) 119103687

[2]. Zhi Y X and Lukasik M and Li M H and Dolatabadi E and Wang R H and Taati B 2017 Automatic detection of compensation during robotic stroke rehabilitation therapy (IEEE journal of translational engineering in health and medicine) 6 1-7

[3]. Kidziński Ł and Yang B and Hicks J L and Rajagopal A and Delp S L and Schwartz M H 2020 Deep neural networks enable quantitative movement analysis using single-camera videos (Nature communications) 11(1) 1-10

[4]. Escalona F and Martinez-Martin E and Cruz E and Cazorla M and Gomez-Donoso F 2020 EVA: EVAluating at-home rehabilitation exercises using augmented reality and low-cost sensors (Virtual Reality) 24(4) 567-581

[5]. Machlin S R and Chevan J and Yu W W and Zodet M W 2011 Determinants of utilization and expenditures for episodes of ambulatory physical therapy among adults (Physical therapy) 91(7) 1018-1029

[6]. Wang S C 2003 Interdisciplinary computing in Java programming (Springer Science & Business Media) Vol. 743

[7]. Baptista R and Ghorbel E and Moissenet F and Aouada D and Douchet A and André M and Bouilland S 2019 Home self-training: Visual feedback for assisting physical activity for stroke survivors (Computer methods and programs in biomedicine) 176 111-120

[8]. Nascimento L M S D and Bonfati L V and Freitas M L B and Mendes Junior J J A and Siqueira H V and Stevan S L 2020 Sensors and systems for physical rehabilitation and health monitoring—A review (Sensors) 20(15) 4063

[9]. Turner C R and Fuggetta A and Lavazza L and Wolf A L and 1999 A conceptual basis for feature engineering (Journal of Systems and Software) 49(1) 3-15

[10]. Tao L and Paiement A and Damen D and Mirmehdi M and Hannuna S and Camplani M and Craddock I 2016 A comparative study of pose representation and dynamics modelling for online motion quality assessment (Computer vision and image understanding) 148 136-152

[11]. Li Y and Chai X and Chen X 2018 ScoringNet: Learning key fragment for action quality assessment with ranking loss in skilled sports (Asian Conference on Computer Vision) pp. 149-164

[12]. Liao Y and Vakanski A and Xian M 2020 A deep learning framework for assessing physical rehabilitation exercises (IEEE Transactions on Neural Systems and Rehabilitation Engineering) 28(2) 468-477

[13]. Bruce X B and Liu Y and Chan K C and Yang Q and Wang X 2021 Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression (Pattern Recognition) 119 108095

[14]. Liang F Y and Zhong C H and Zhao X and Castro D L and Chen B and Gao F and Liao W H 2018 Online adaptive and lstm-based trajectory generation of lower limb exoskeletons for stroke rehabilitation (2018 IEEE International Conference on Robotics and Biomimetics) pp. 27-32

[15]. Qiu Y et al. 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control 72 103323

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