Abstract
Angle assessment is crucial in rehabilitation and significantly influences physiotherapists’ decision-making. Although visual inspection is commonly used, it is known to be approximate. This work aims to be a preliminary study about using the AI image-based to assess upper limb joint angles. Two main frameworks were evaluated: MediaPipe and Yolo v7. The study was performed with 28 participants performing four upper limb movements. The results showed that Yolo v7 achieved greater estimation accuracy than Mediapipe, with MAEs of around \(5^\circ \) and \(17^\circ \), respectively. However, even with better results, Yolo v7 showed some limitations, including the point of detection in only a 2D plane, the higher computational power required to enable detection, and the difficulty of performing movements requiring more than one degree of Freedom (DOF). Nevertheless, this study highlights the detection capabilities of AI approaches, showing be a promising approach for measuring angles in rehabilitation activities, representing a cost-effective and easy-to-implement solution.
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References
Klopčar, N., Lenarčič, J.: Kinematic model for determination of human arm reachable workspace. Meccanica 40, 203–219 (2005)
Liu, L., et al.: Kinematics analysis of arms in synchronized canoeing with wearable inertial measurement unit. IEEE Sens. J. (2023)
Liu, R., Liu, C.: Human motion prediction using adaptable recurrent neural networks and inverse kinematics. IEEE Control Syst. Lett. 5(5), 1651–1656 (2020)
O’Sullivan, S.B., Schmitz, T.J.: Physical Rehabilitation. 5th edn (2007)
Kelley, M.J., Mcclure, P.W., Leggin, B.G.: Frozen shoulder: evidence and a proposed model guiding rehabilitation. J. Orthopaedic Sports Phys. Therapy 39(2), 135–148 (2009)
Olsen, T.S.: Arm and leg paresis as outcome predictors in stroke rehabilitation. Stroke 21(2), 247–251 (1990)
Qassim, H.M., Hasan, W.Z.W.: A review on upper limb rehabilitation robots. Appl. Sci. 10(19), 6976 (2020)
Krebs, H.I., Volpe, B.T.: Chapter 23 - Rehabilitation robotics. In: Barnes, M.P., Good, D.C. (eds.) Neurological Rehabilitation. Handbook of Clinical Neurology, vol. 110, pp. 283–294. Elsevier, Amsterdam (2013)
Argent, R., Drummond, S., Remus, A., O’Reilly, M., Caulfield, B.: Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor. J. Rehabil. Assist. Technol. Eng. 6 (2019)
Wochatz, M., et al.: Reliability and validity of the kinect v2 for the assessment of lower extremity rehabilitation exercises. Gait Posture 70, 330–335 (2019)
Denecke, K., Baudoin, C.R.: A review of artificial intelligence and robotics in transformed health ecosystems. Front. Med. 9 (2022)
Luxton, D.D., Riek, L.D.: Artificial intelligence and robotics in rehabilitation (2019)
Yuan, F., Klavon, E., Liu, Z., Lopez, R.P., Zhao, X.: A systematic review of robotic rehabilitation for cognitive training. Front. Robot. AI 8 (2021)
Phan, G.H., Solanki, V.K., Quang, N.H.: Artificial intelligence in rehabilitation evaluation-based robotic exoskeletons: a review. In: Bio-inspired Motor Control Strategies for Redundant and Flexible Manipulator with Application to Tooling Tasks. SAST, pp. 79–91. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-9551-3_6
Kaelin, V.C., Valizadeh, M., Salgado, Z., Parde, N., Khetani, M.A.: Artificial intelligence in rehabilitation targeting the participation of children and youth with disabilities: scoping review. J. Med. Internet Res. 23(11), e25745 (2021)
Bermejo, C., Pan, H.: A survey on haptic technologies for mobile augmented reality. ACM Comput. Surv. 54(9), 1–35 (2022)
Leardini, A., Lullini, G., Giannini, S., Berti, L., Ortolani, M., Caravaggi, P.: Validation of the angular measurements of a new inertial-measurement-unit based rehabilitation system: comparison with state-of-the-art gait analysis. J. Neuroeng. Rehabil. 11(1), 1–7 (2014)
Daponte, P., De Vito, L., Riccio, M., Sementa, C.: Design and validation of a motion-tracking system for rom measurements in home rehabilitation. Measurement 55, 82–96 (2014)
Boato, G., Conci, N., Daldoss, M., De Natale, F.G.B., Piotto, N.: Hand tracking and trajectory analysis for physical rehabilitation. In: 2009 IEEE International Workshop on Multimedia Signal Processing, pp. 1–6 (2009)
Zestas, O.N., Soumis, D.N., Kyriakou, K.D., et al.: A computer-vision based hand rehabilitation assessment suite. AEU - Int. J. Electron. Commun. 169, 154762 (2023)
Lin, G., Wu, W., Lin, C., Song, Y., Xie, L., Cai, S.: A vision-based compensation detection approach during robotic stroke rehabilitation therapy. In: 3rd International Academic Exchange Conference on Science and Technology Innovation, IAECST 2021, pp. 768–771 (2021)
Russell, S.J.: Artificial Intelligence a Modern Approach. Pearson Education Inc, London (2010)
Lugaresi, C., et al.: Mediapipe: a framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019)
Bazarevsky, V., Grishchenko, I., Raveendran, K., Zhu, T., Zhang, F., Grundmann, M.: Blazepose: on-device real-time body pose tracking. arXiv preprint arXiv:2006.10204 (2020)
Bazarevsky, I., Grishchenko, V.: On-device, real-time body pose tracking with mediapipe blazepose, June 2023. https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. CoRR, abs/1506.02640 (2015)
Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696 (2022)
Wang, C.-Y., Liao, H.-Y.M., Yeh, I.-H.: Designing network design strategies through gradient path analysis. arXiv preprint arXiv:2211.04800 (2022)
Chellal, A.A., et al.: Smarthealth: a robotic control software for upper limb rehabilitation. In: Brito Palma, L., Neves-Silva, R., Gomes, L. (eds.) CONTROLO 2022. LNNE, vol. 930, pp. 667–676. Springer, Cham (2022)
Chellal, A.A., et al.: Robot-assisted rehabilitation architecture supported by a distributed data acquisition system. Sensors 22(23), 9532 (2022)
Acknowledgements
This work has been supported by SmartHealth - Inteligência Artificial para Cuidados de Saúde Personalizados ao Longo da Vida, under the project ref. NORTE-01-0145-FEDER-000045. The authors are grateful to the Foundation for Science and Technology (FCT) for financial support under ref. FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Arezki A. Chellal is grateful to the FCT Foundation for its support through the FCT PhD scholarship with ref. UI/BD/154484/2022.
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Klein, L.C. et al. (2024). Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1982 . Springer, Cham. https://doi.org/10.1007/978-3-031-53036-4_1
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