Abstract
Percussion therapy has positive effects on the human body, such as increasing strength, improving range of motion and flexibility. This is due to the vibrations it produces, which when applied to the human body, cause vasodilatory responses. The main contribution of this work is a methodology based on infrared thermography and convolutional neural network to evaluate and classify the range of motion in patients undergoing hamstring percussion therapy. It consists of three steps: (1) data preparation, (2) data augmentation, and (3) convolutional neural network design and validation. In the data preparation step, 50 images acquired in the pre- and post-percussion therapy phases were pooled, and then each image was labeled in one of the two classes: Within and outside range of motion. Data augmentation, including techniques such as flipping and contrast adjustment, was used to expand the dataset. A convolutional neural network model was used to classify the images. When evaluated on a test set, an accuracy of 90.48% was obtained for the classification of 2 classes. These results demonstrate the efficacy and robustness of the approach to assess range of motion in patients undergoing hamstring percussion therapy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Skinner, B., Dunn, L., Moss, R.: The acute effects of theraguntm percussive therapy on viscoelastic tissue dynamics and hamstring group range of motion. J. Sports Sci. Med. 496–501 (2023). https://doi.org/10.52082/jssm.2023.496
El-berkawy, H.: Effect of percussion massage gun on hamstring flexibility in patients with knee osteoarthritis: a randomized controlled trial. Benha Int. J. Phys. Therapy 1(1), 1–9 (2023). https://doi.org/10.21608/bijpt.2023.255236.1000
Ferreira, R.M., et al.: The effects of massage guns on performance and recovery: a systematic review. J. Funct. Morphol. Kinesiol. 8(3), 138 (2023). https://doi.org/10.3390/jfmk8030138
Pilch, W., et al.: The impact of vibration therapy interventions on skin condition and skin temperature changes in young women with lipodystrophy: a pilot study. Evid.-Based Complement. Alternat. Med. 2019, 1–9 (2019). https://doi.org/10.1155/2019/8436325
Côrte, A.C. Pedrinelli, A., Marttos, A., Souza, I.F.G., Grava, J., José Hernandez, A.: Infrared thermography study as a complementary method of screening and prevention of muscle injuries: pilot study. BMJ Open Sport Exerc. Med. 5(1), e000431 (2019). https://doi.org/10.1136/bmjsem-2018-000431
Trejo-Chavez, O., Priego-Quesada, J.I., Gonzalez-Hernandez, M.P., Morales-Hernandez, L.A., Cruz-Albarran, I.A.: Knee skin temperature response of patients with bilateral patellofemoral syndrome before and after heat and cold stress. J. Therm. Biol. 115, 103601 (2023). https://doi.org/10.1016/j.jtherbio.2023.103601
Alshehri, A., AlSaeed, D.: Breast cancer detection in thermography using Convolutional Neural Networks (CNNs) with deep attention mechanisms. Appl. Sci. 12(24), 12922 (2022). https://doi.org/10.3390/app122412922
de Freitas Barbosa, V.A., de Santana, M.A., Andrade, M.K.S., de Lima, C.F., dos Santos, W.P.: Deep-wavelet neural networks for breast cancer early diagnosis using mammary termographies. In: Deep Learning for Data Analytics, ch. 6, pp. 99–124. Elsevier (2020). https://doi.org/10.1016/B978-0-12-819764-6.00007-7
Farooq, M.A., Corcoran, P.: Infrared imaging for human thermography and breast tumor classification using thermal images. In: 2020 31st Irish Signals and Systems Conference (ISSC), pp. 1–6. IEEE (2020). https://doi.org/10.1109/ISSC49989.2020.9180164
Mohamed, E.A., Rashed, E.A., Gaber, T., Karam, O.: Deep learning model for fully automated breast cancer detection system from thermograms. PLoS ONE 17(1), e0262349 (2022). https://doi.org/10.1371/journal.pone.0262349
Filipe, V., Teixeira, P., Teixeira, A.: Automatic classification of foot thermograms using machine learning techniques. Algorithms 15(7), 236 (2022). https://doi.org/10.3390/a15070236
Trejo-Chavez, O., Amezquita-Sanchez, J.P., Huerta-Rosales, J.R., Morales-Hernandez, L.A., Cruz-Albarran, I.A., Valtierra-Rodriguez, M.: Automatic knee injury identification through thermal image processing and convolutional neural networks. Electronics (Basel) 11(23), 3987 (2022). https://doi.org/10.3390/electronics11233987
Ayala, F., Sainz de Baranda, P., Cejudo, A., Santonja, F.: Pruebas angulares de estimación de la flexibilidad isquiosural: descripción de los procedimientos exploratorios y valores de referencia. Rev Andal Med Deport 6(3), 120–128 (2013). https://doi.org/10.1016/S1888-7546(13)70046-7
de Lucena, G.L., dos Santos Gomes, C., Guerra, R.o.: Prevalence and associated factors of osgood-schlatter syndrome in a population-based sample of Brazilian adolescents. Am. J. Sports Med. 39(2), 415–420 (2011). https://doi.org/10.1177/0363546510383835
Miñarro, P.A.L., GarcÃa, P.L.R., Lucas, J.L.Y., Cárceles, F.A., Fiol, C.F.: Validez de la posición del raquis lumbo-sacro en flexión como criterio de extensibilidad isquiosural en deportistas jóvenes (2008). https://api.semanticscholar.org/CorpusID:140877519
Konrad, A., Glashüttner, C., Reiner, M.M., Bernsteiner, D., Tilp, M.: The acute effects of a percussive massage treatment with a hypervolt device on plantar flexor muscles’ range of motion and performance. J. Sports Sci. Med. 19(4), 690 (2020)
Cheatham, S.W., Baker, R.T., Behm, D.G., Stull, K., Kolber, M.J.: Mechanical percussion devices: a survey of practice patterns among healthcare professionals. Int. J. Sports Phys. Ther. 16(3) (2021). https://doi.org/10.26603/001c.23530
Acknowledgments
Alejandra Vilchis Yubi would like to thank the Mexican Council of Humanities, Science and Technology for the scholarship 1313994.
Author information
Authors and Affiliations
Contributions
The authors declare no competing interests.
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Vilchis-Yubi, A., Cedeno-Moreno, R., Espino-Gonzalez, J.A., Mancilla-Morales, A., Morales-Hernandez, L.A., Cruz-Albarran, I.A. (2025). Assessment of Range of Motion Before and After Hamstring Percussion Therapy Using Thermography and CNN. In: Kakileti, S.T., Manjunath, G., Schwartz, R.G., Ng, E.Y.K. (eds) Artificial Intelligence over Infrared Images for Medical Applications. AIIIMA 2024. Lecture Notes in Computer Science, vol 15279. Springer, Cham. https://doi.org/10.1007/978-3-031-76584-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-76584-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-76583-4
Online ISBN: 978-3-031-76584-1
eBook Packages: Computer ScienceComputer Science (R0)