Convolutional Neural Network in Motion Detection for Physiotherapy Exercise Movement

Dika Fikri Laistulloh; Anik Nur Handayani; Rosa Andrie Asmara; Phillip Taw

doi:10.17977/um018v7i12024p27-39

Convolutional Neural Network in Motion Detection for Physiotherapy Exercise Movement

Dika Fikri Laistulloh, Anik Nur Handayani, Rosa Andrie Asmara, Phillip Taw

Abstract

Physiotherapy focuses on movement and optimal utilization of the patient's potential. Exercise Therapy is a physiotherapy procedure that specifically focuses exercises on active and passive movements. Cerebral Palsy (CP) patients are one of the sufferers of motor disorders of the upper extremities. Cerebral Palsy (CP) patients suffer from disorders in motor functions of the upper extremities. Physiotherapy Exercise Movement has 4 categories of movement exercises for the therapy of people with upper extremity body disorders: Elbow flexor strengthening in sitting using free weights, lifting an object up, reaching diagonally in sitting, and reaching from a low surface to a high surface. By taking 4 categories of motion movements in exercise therapy, data were taken using normal child subjects as standard movements, which then became a reference for CP child therapy. The limitations of therapy in physical care prompted researchers to investigate the use of image processing as input to Human Computer Interaction (HCI) in the process of motion detection-based therapy. In research using Deep learning as a classifier, namely using the CNN Model (Inception V3, Resnet152, and VGG16 architectural models). The results obtained by the CNN (Inception V3) model have the best performance with an accuracy percentage of 98%.

Full Text:

PDF

References

A. Gonzalez, L. Garcia, J. Kilby, and P. McNair, “Robotic devices for paediatric rehabilitation: a review of design features,” Biomed. Eng. Online, vol. 20, no. 1, p. 89, Sep. 2021.

D. O’Neill and D. E. Forman, “The importance of physical function as a clinical outcome: Assessment and enhancement,” Clin. Cardiol., vol. 43, no. 2, pp. 108–117, Feb. 2020.

R. Bouça‐Machado et al., “Physical Activity, Exercise, and Physiotherapy in Parkinson’s Disease: Defining the Concepts,” Mov. Disord. Clin. Pract., vol. 7, no. 1, pp. 7–15, Jan. 2020.

V. Riccio, G. Jahangirova, A. Stocco, N. Humbatova, M. Weiss, and P. Tonella, “Testing machine learning based systems: a systematic mapping,” Empir. Softw. Eng., vol. 25, no. 6, pp. 5193–5254, Nov. 2020.

C. Rossa, M. Najafi, M. Tavakoli, and K. Adams, “Robotic Rehabilitation and Assistance for Individuals With Movement Disorders Based on a Kinematic Model of the Upper Limb,” IEEE Trans. Med. Robot. Bionics, vol. 3, no. 1, pp. 190–203, Feb. 2021.

C. Marquez-Chin and M. R. Popovic, “Functional electrical stimulation therapy for restoration of motor function after spinal cord injury and stroke: a review,” Biomed. Eng. Online, vol. 19, no. 1, p. 34, Dec. 2020.

A. A. Barati, R. Rajabi, S. Shahrbanian, and M. Sedighi, “Investigation of the effect of sensorimotor exercises on proprioceptive perceptions among children with spastic hemiplegic cerebral palsy,” J. Hand Ther., vol. 33, no. 3, pp. 411–417, Jul. 2020.

W. Trusaji et al., “Horse Riding Simulator Design to Replicate Human Walking Gait for Hippotherapy in Cerebral Palsy Rehabilitation,” Machines, vol. 10, no. 11, p. 1060, Nov. 2022.

R. Llamas-Ramos, J. L. Sánchez-González, and I. Llamas-Ramos, “Robotic Systems for the Physiotherapy Treatment of Children with Cerebral Palsy: A Systematic Review,” Int J Env. Res Public Health, 2022.

M. J. Vinolo-Gil, E. Casado-Fernández, V. Perez-Cabezas, G. Gonzalez-Medina, F. J. Martín-Vega, and R. Martín-Valero, “Effects of the Combination of Music Therapy and Physiotherapy in the Improvement of Motor Function in Cerebral Palsy: A Challenge for Research,” Children, vol. 8, no. 10, p. 868, Sep. 2021.

A. S. Faradyza et al., “Real Time Gesture Detection Using Kinect in Rehabilitation Therapy for Children with Disability,” in 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Oct. 2021, pp. 452–456.

K. Muhammad et al., “Human action recognition using attention based LSTM network with dilated CNN features,” Future Gener. Comput. Syst., vol. 125, pp. 820–830, Dec. 2021.

T. S. Gunawan et al., “Development of video-based emotion recognition using deep learning with Google Colab,” TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 18, no. 5, p. 2463, Oct. 2020.

N. Jaouedi, N. Boujnah, and M. S. Bouhlel, “A new hybrid deep learning model for human action recognition,” J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 4, pp. 447–453, May 2020.

G. Divya and S. Chokkalingam, “Human Action Recognition In Streaming Video Using Deep Learning,” Int. J. Adv. Sci. Technol., vol. 29, no. 9, 2020.

D. Chen, S. Shi, X. Gu, and B. Shim, “Robust DoA Estimation Using Denoising Autoencoder and Deep Neural Networks,” IEEE Access, vol. 10, pp. 52551–52564, 2022.

S. D. Thepade, M. Dindorkar, P. Chaudhari, and S. Bang, “Face presentation attack identification optimization with adjusting convolution blocks in VGG networks,” Intell. Syst. Appl., vol. 16, p. 200107, Nov. 2022.

Y. Abdulazeem, H. M. Balaha, W. M. Bahgat, and M. Badawy, “Human Action Recognition Based on Transfer Learning Approach,” IEEE Access, vol. 9, pp. 82058–82069, 2021.

P. Kuppusamy and V. C. Bharathi, “Human abnormal behavior detection using CNNs in crowded and uncrowded surveillance – A survey,” Meas. Sens., vol. 24, p. 100510, Dec. 2022.

S. Gupta, “Deep learning based human activity recognition (HAR) using wearable sensor data,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 2, p. 100046, Nov. 2021.

V. Ghate and S. H. C, “Hybrid deep learning approaches for smartphone sensor-based human activity recognition,” Multimed. Tools Appl., vol. 80, no. 28–29, pp. 35585–35604, Nov. 2021.

V. B. Semwal, A. Gupta, and P. Lalwani, “An optimized hybrid deep learning model using ensemble learning approach for human walking activities recognition,” J. Supercomput., vol. 77, no. 11, pp. 12256–12279, Nov. 2021.

K. D. McCay, E. S. L. Ho, H. P. H. Shum, G. Fehringer, C. Marcroft, and N. D. Embleton, “Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features,” IEEE Access, vol. 8, pp. 51582–51592, 2020.

S. Hechmi, “An Accurate Real-Time Method for Face Mask Detection using CNN and SVM,” Knowl. Eng. Data Sci., vol. 5, no. 2, p. 129, Dec. 2022.

Y. Adiwinata, A. Sasaoka, I. P. Agung Bayupati, and O. Sudana, “Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset,” Lontar Komput. J. Ilm. Teknol. Inf., vol. 11, no. 3, p. 144, Dec. 2020.

G. Chen, D. Ye, Z. Xing, J. Chen, and E. Cambria, “Ensemble application of convolutional and recurrent neural networks for multi-label text categorization,” in 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA: IEEE, May 2017, pp. 2377–2383.

I. P. Kanani and M. Padole, “Deep Learning to Detect Skin Cancer using Google Colab,” Int. J. Eng. Adv. Technol., vol. 8, no. 6, pp. 2176–2183, Aug. 2019.

K. Joshi, V. Tripathi, C. Bose, and C. Bhardwaj, “Robust Sports Image Classification Using InceptionV3 and Neural Networks,” Procedia Comput. Sci., vol. 167, pp. 2374–2381, 2020.

Y. Li and L. Wang, “Human Activity Recognition Based on Residual Network and BiLSTM,” Sensors, vol. 22, no. 2, p. 635, Jan. 2022.

S. Raziani and M. Azimbagirad, “Deep CNN hyperparameter optimization algorithms for sensor-based human activity recognition,” Neurosci. Inform., vol. 2, no. 3, p. 100078, Sep. 2022.

H. Yu, X. Miao, and H. Wang, “Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field,” Sensors, vol. 22, no. 5, p. 1705, Feb. 2022.

T. B. Abdallah, I. Elleuch, and R. Guermazi, “Student Behavior Recognition in Classroom using Deep Transfer Learning with VGG-16,” Procedia Comput. Sci., vol. 192, pp. 951–960, 2021.

DOI: http://dx.doi.org/10.17977/um018v7i12024p27-39