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Muscle intent-based continuous passive motion machine in a gaming context using a lightweight CNN

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Abstract

This paper presents a novel approach to control and actuate a Continuous Passive Motion (CPM) machine by integrating a deep learning-based control strategy using convolutional neural networks in a gaming context for providing post-surgical therapy and knee rehabilitation. Electromyography and inertial measurement unit sensors are interfaced with the patient's thigh muscles to record the patient's intent signals and classify them as three states: forward, backward, and rest. Comparison studies have been performed to prove the novelty of the proposed lightweight convolutional neural network architecture over other architectures and machine learning methodologies for real-time implementation. Additionally, gaming software has been interfaced, making the recovery process motivating to deal with the psychological aspects of rehabilitation. A low-cost, ecofriendly alpha prototyped CPM machine is prototyped for implementing the algorithms. Experiments are performed on three healthy subjects to establish the feasibility of this home rehabilitation device under professional guidance. Thus, this study aims to improve home-based knee rehabilitation effectiveness, offering complete recovery to the patients, delivering intensive and motivational rehabilitation.

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Data availability

The data set used and/or analyzed during the current study is available from the corresponding author on reasonable request. Demo video of the game: https://youtu.be/hVgx0V7KVVM.

Code availability

The code used during the current study is available from the corresponding author upon reasonable request.

References

  • Amsuss, S., Goebel, P.M., Jiang, N., et al.: Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control. IEEE Trans. Biomed. Eng. 61, 1167–1176 (2014). https://doi.org/10.1109/TBME.2013.2296274

    Article  Google Scholar 

  • Birch, B., Haslam, E., Heerah, I., Dechev, N., Park, E.J.: Design of a continuous passive and active motion device for hand rehabilitation. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2008, 4306–4309 (2008). https://doi.org/10.1109/IEMBS.2008.4650162

    Article  Google Scholar 

  • Dantas, H., Warren, D.J., Wendelken, S.M., et al.: Deep learning movement intent decoders trained with dataset aggregation for prosthetic limb control. IEEE Trans. Biomed. Eng. 66, 3192–3203 (2019). https://doi.org/10.1109/TBME.2019.2901882

    Article  Google Scholar 

  • Davis AM, MacKay C.: Osteoarthritis year in review: outcome of rehabilitation. Osteoarthritis Cartilage. 2013;21(10):1414–24. https://doi.org/10.1016/j.joca.2013.08.013, (2013).

  • Deyle GD, Gill NW.:Well-tolerated strategies for managing knee osteoarthritis: A manual physical therapist approach to activity, exercise, and advice. Phys Sportsmed 40:. https://doi.org/10.3810/psm.2012.09.1976, (2012)

  • E S, K E.:Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J Rehabil Res Dev 48:643–660. https://doi.org/10.1682/JRRD.2010.09.0177,(2011).

  • Fisher NM, Pendergast DR, Gresham GE, Calkins E. Muscle rehabilitation: its effect on muscular and functional performance of patients with knee osteoarthritis. Arch. Phys. Med. Rehabil. 72(6):367–74. PMID: 2059102, (1991).

  • Gassert, R., Dietz, V.: Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective. J. Neuroeng. Rehabil. 15, 1–15 (2018)

    Article  Google Scholar 

  • Golgouneh A, Bamshad A, Tarvirdizadeh B, Tajdari F.:Design of a new, light and portable mechanism for knee CPM machine with a user-friendly interface. In: 2016 Artificial Intelligence and Robotics, IRANOPEN 2016. Institute of Electrical and Electronics Engineers Inc., pp 103–108, (2016)

  • Hakonen, M., Piitulainen, H., Visala, A.: Current state of digital signal processing in myoelectric interfaces and related applications. Biomed. Signal Process. Control 18, 334–359 (2015)

    Article  Google Scholar 

  • Hargrove, L.J., Englehart, K., Hudgins, B.: A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans. Biomed. Eng. 54, 847–853 (2007). https://doi.org/10.1109/TBME.2006.889192

    Article  Google Scholar 

  • He K, Zhang X, Ren S, Sun J.:Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016-December:770–778. https://doi.org/10.1109/CVPR.2016.90, (2016)

  • Ivarsson, A., Tranaeus, U., Johnson, U., Stenling, A.: Negative psychological responses of injury and rehabilitation adherence effects on return to play in competitive athletes: a systematic review and meta-analysis. Open Access J Sport Med 8, 27–32 (2017). https://doi.org/10.2147/oajsm.s112688

    Article  Google Scholar 

  • J L, X S, D Z, et al.:Towards Zero Retraining for Myoelectric Control Based on Common Model Component Analysis. IEEE Trans Neural Syst Rehabil Eng 24:444–454. https://doi.org/10.1109/TNSRE.2015.2420654,(2016).

  • JW S, BA L, TA K.:Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms. IEEE Trans Neural Syst Rehabil Eng 17:270–278. https://doi.org/10.1109/TNSRE.2009.2023282, (2009).

  • Kansal, S., Garg, D., Upadhyay, A., et al.: A novel deep learning approach to predict subject arm movements from EEG-based signals. Neural Comput. & Applic. 35(11669–11679), 2023 (2023a). https://doi.org/10.1007/s00521-023-08310-9

    Article  Google Scholar 

  • Kansal, S., Jha, S., Samal, P.: DL-DARE: Deep learning-based different activity recognition for the human–robot interaction environment. Neural Comput. & Applic. 35, 12029–12037 (2023b). https://doi.org/10.1007/s00521-023-08337-y

    Article  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE.:ImageNet classification with deep convolutional neural networks. Commun ACM 60:84–90. https://doi.org/10.1145/3065386, (2017).

  • Le N-B, Nguyen H-N, Nguyen D-A, Vo H-D.:Study on Mechanical Adaptive Design, Construction and Control of Knee Continuous Passive Motion Machine. J Autom Control Eng 1:227–231. https://doi.org/10.12720/joace.1.3.227-231,(2013).

  • LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2323 (1998). https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  • Lenssen TAF, Van Steyn MJA, Crijns YHF, et al.:Effectiveness of prolonged use of continuous passive motion (CPM), as an adjunct to physiotherapy, after total knee arthroplasty. BMC Musculoskelet Disord 9:. https://doi.org/10.1186/1471-2474-9-60, (2008).

  • Lenzi T, De Rossi SMM, Vitiello N, Carrozza MC.:Intention-based EMG control for powered exoskeletons. IEEE Trans Biomed Eng 59:2180–2190. https://doi.org/10.1109/TBME.2012.2198821, (2012).

  • Lyu, M., Chen, W.H., Ding, X., et al.: Development of an EMG-controlled knee exoskeleton to assist home rehabilitation in a game context. Front. Neurorobot. 13, 67 (2019a). https://doi.org/10.3389/fnbot.2019.00067

    Article  Google Scholar 

  • Mingxing Lyu, Wei-Hai Chen, Xilun Ding, Jianhua Wang, Zhongcai Pei, Baochang Zhang.:Development of an EMG-Controlled Knee Exoskeleton to Assist Home Rehabilitation in a Game Context, Front. Neurorobot., Vol-13,https://doi.org/10.3389/fnbot.2019.00067, (2019).

  • McGlinchey, M.P., James, J., McKevitt, C., et al.: The effect of rehabilitation interventions on physical function and immobility-related complications in severe stroke—Protocol for a systematic review. Syst. Rev. 7, 197 (2018). https://doi.org/10.1186/s13643-018-0870-y

    Article  Google Scholar 

  • MM V, HJ H, S A, et al.:Improving the Robustness of Myoelectric Pattern Recognition for Upper Limb Prostheses by Covariate Shift Adaptation. IEEE Trans Neural Syst Rehabil Eng 24:961–970. https://doi.org/10.1109/TNSRE.2015.2492619, (2016).

  • Naeem UJ, Xiong C, Abdullah AA.:EMG-muscle force estimation model based on back-propagation neural network. In: Proceedings of IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems, VECIMS. pp 222–227, (2012).

  • Neogi, T.: The epidemiology and impact of pain in osteoarthritis. Osteoarthr. Cartil. 21, 1145–1153 (2013). https://doi.org/10.1016/j.joca.2013.03.018

    Article  Google Scholar 

  • Peternel, L., Noda, T., Petrič, T., et al.: Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation. PLoS ONE 11, e0148942 (2016). https://doi.org/10.1371/journal.pone.0148942

    Article  Google Scholar 

  • Rajestari Z, Feizi N, Taghvaei S.:Kinematic synthesis and optimization of Continuous Passive Motion mechanisms for knee. In: 2017 7th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2017. Institute of Electrical and Electronics Engineers Inc.,(2017)

  • Rivera HRA, Ortega AB, Bautista RV, et al.:CPM ankle rehabilitation machine with EMG signal analysis. In: Proceedings—2013 international conference on mechatronics, electronics and automotive engineering, ICMEAE 2013. pp 164–170, (2013).

  • Saponas TS, Tan DS, Morris D, et al.:Making muscle-computer interfaces more practical. In: Conference on human factors in computing systems—proceedings. pp 851–854, (2010).

  • Simonyan K, Zisserman A, very deep convolutional networks for large-scale image recognition. In: Proceedings of international conference on learning representations, https://doi.org/10.48550/arXiv.1409.1556, (2015).

  • Srinivasan, V.B., Islam, M., Zhang, W., Ren, H.: Finger movement classification from myoelectric signals using convolutional neural networks. IEEE Int Conf Robot Biomimetics, ROBIO 2018, 1070–1075 (2018). https://doi.org/10.1109/ROBIO.2018.8664807

    Article  Google Scholar 

  • Suthar B, Zubair M, Kansal S, Mukherjee S Design of Adaptive Sensor Coupling-Based Upper Limb 7-DOF Exoskeleton for Smooth Human Motion Tracking: ASC-EXO, in IEEE Sensors Journal, vol. 23, no. 18, pp. 20607–20618, https://doi.org/10.1109/JSEN.2023.3270172 (2023).

  • Tangjitsitcharoen, S., Lohasiriwat, H.: Redesign of a continuous passive motion machine for total knee replacement therapy. J Heal Res 33, 106–118 (2019). https://doi.org/10.1108/JHR-06-2018-0024

    Article  Google Scholar 

  • Toledo-Pérez DC, Martínez-Prado MA, Gómez-Loenzo RA, et al. A study of movement classification of the lower limb based on up to 4-EMG channels. Electron 8:. https://doi.org/10.3390/electronics8030259,(2019).

  • Trochimczuk R, Kuźmierowski T.:Kinematic Analysis of Cpm Machine Supporting to Rehabilitation Process after Surgical Knee Arthroscopy and Arthroplasty. Int J Appl Mech Eng 19:841–848. https://doi.org/10.2478/ijame-2014-0059, (2014)

  • V. K. Viekash et al.: Deep Learning Based Muscle Intent Classification in Continuous Passive Motion Machine for Knee Osteoarthritis Rehabilitation. 2021 IEEE Madras Section Conference (MASCON), pp. 1–8 (2021).

  • Walton E, Casey C, Mitsch J, et al.:Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour. R Soc Open Sci 5:. https://doi.org/10.1098/rsos.171442,(2018).

  • X C, D Z, X Z.:Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control. J Neuroeng Rehabil 10:. https://doi.org/10.1186/1743-0003-10-44,(2013).

  • Zhai X, Jelfs B, Chan RHM, Tin C.: Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network. in Front Neurosci.,11:379. https://doi.org/10.3389/fnins.2017.00379, (2017).

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Funding

This research was funded in part by the SERB-POWER Grant, Department of Science and Technology, Govt. of India with File no: NITT/SPG/2021/01420.

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Correspondence to Ezhilarasi Deenadayalan.

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The Medical Ethics Committee approves the study protocol of the National Institute of Technology Tiruchirappalli.

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Appendix

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Table 9 Activity scenarios and participant states in dataset generation for 3 participants

9

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Viekash, V.K., Deenadayalan, E. Muscle intent-based continuous passive motion machine in a gaming context using a lightweight CNN. Int J Intell Robot Appl (2024). https://doi.org/10.1007/s41315-024-00369-4

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