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Upper-Limb Robotic Exoskeleton for Early Cardiac Rehabilitation Following an Open-Heart Surgery—Mathematical Modelling and Empirical Validation

Author

Listed:
  • Bogdan Mocan

    (Department of Design Engineering and Robotics, Technical University of Cluj-Napoca, 400020 Cluj-Napoca, Romania)

  • Claudiu Schonstein

    (Department of Mechanical Systems Engineering, Technical University of Cluj-Napoca, 400020 Cluj-Napoca, Romania)

  • Mircea Murar

    (Department of Design Engineering and Robotics, Technical University of Cluj-Napoca, 400020 Cluj-Napoca, Romania)

  • Calin Neamtu

    (Department of Design Engineering and Robotics, Technical University of Cluj-Napoca, 400020 Cluj-Napoca, Romania)

  • Mircea Fulea

    (Department of Design Engineering and Robotics, Technical University of Cluj-Napoca, 400020 Cluj-Napoca, Romania)

  • Mihaela Mocan

    (Department of Internal Medicine, University of Medicine and Pharmacy Iuliu Hatieganu, 400012 Cluj-Napoca, Romania)

  • Simona Dragan

    (Department of Cardiology, Clinic of Cardiovascular Prevention and Rehabilitation, University of Medicine and Pharmacy Victor Babes, 300041 Timisoara, Romania)

  • Horea Feier

    (Institute for Cardiovascular Diseases Timisoara, Victor Babes University of Medicine and Pharmacy Timisoara, Gheorghe Adam Nr. 13A, 300310 Timisoara, Romania)

Abstract

Robotic exoskeletons have the potential to enhance the quality of life of patients undergoing cardiac rehabilitation. Recent studies found that the use of such devices was associated with significant improvements in physical function, mobility, and overall well-being for individuals recovering from a cardiac event. These improvements were seen across a range of measures, including cardiovascular fitness, muscle strength, and joint range of motion. In addition, the use of robotic exoskeletons may help to accelerate the rehabilitation process, allowing patients to make faster progress towards their goals. This article proposes a new robotic exoskeleton structure with 12 DOFs (6 DOFs on each arm) in a symmetrical construction for upper limbs intended to be used in the early rehabilitation of cardiac patients following open-heart surgery or a major cardiac event. The mathematical modelling and empirical validation of the robotic exoskeleton prototype are described. The matrix exponential algorithm, kinetic energy, and generalized forces were employed to overcome the problem of high complexity regarding the kinematic and dynamic model of the robotic exoskeleton. The robotic exoskeleton prototype was empirically validated by assessing its functionalities in a lab and medical environment.

Suggested Citation

  • Bogdan Mocan & Claudiu Schonstein & Mircea Murar & Calin Neamtu & Mircea Fulea & Mihaela Mocan & Simona Dragan & Horea Feier, 2023. "Upper-Limb Robotic Exoskeleton for Early Cardiac Rehabilitation Following an Open-Heart Surgery—Mathematical Modelling and Empirical Validation," Mathematics, MDPI, vol. 11(7), pages 1-43, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1598-:d:1107530
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    References listed on IDEAS

    as
    1. Mihaela Mocan & Sonia Irina Vlaicu & Anca Daniela Farcaș & Horea Feier & Simona Dragan & Bogdan Mocan, 2021. "Cardiac Rehabilitation Early after Sternotomy Using New Assistive VR-Enhanced Robotic Exoskeleton—Study Protocol for a Randomised Controlled Trial," IJERPH, MDPI, vol. 18(22), pages 1-12, November.
    2. Philipp Bader & Sergio Blanes & Fernando Casas, 2019. "Computing the Matrix Exponential with an Optimized Taylor Polynomial Approximation," Mathematics, MDPI, vol. 7(12), pages 1-19, December.
    3. Bogdan Mocan & Mihaela Mocan & Mircea Fulea & Mircea Murar & Horea Feier, 2022. "Home-Based Robotic Upper Limbs Cardiac Telerehabilitation System," IJERPH, MDPI, vol. 19(18), pages 1-21, September.
    4. Alkaff, Abdullah & Qomarudin, Mochamad Nur & Bilfaqih, Yusuf, 2020. "Network reliability analysis: Matrix-exponential approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Jieyi Yi & Niansheng Tang, 2022. "Variational Bayesian Inference in High-Dimensional Linear Mixed Models," Mathematics, MDPI, vol. 10(3), pages 1-19, January.
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