A concept of detecting patient hazards during exoskeleton-aided remote home motor rehabilitation

• Autorzy: Wilk Julia , Falkowski Piotr
• Języki publikacji: EN

Abstrakty

EN

The paper presents a concept of detecting dangerous situations for the patient during exoskeleton-aided home remote rehabilitation. For this purpure, a literature review was conducted to define potential traumas with corresponding causes, measuring approaches and the method of modelling based on these two to assess the risk during treatment. The original concept is based on a numerically modelled digital twin of a patient and an exoskeleton. It consists of a multibody model of a skeletal system and the mechatronic device combined with the soft tissue advanced models. Moreover, the implementation of neural networks and biosignals tracking is suggested in order to predict hazards instead of just monitoring them in real-time. The presented solution can be created within the OpenSim environment. The advantages and challenges of this approach are also discussed.

Słowa kluczowe

EN

medical robots; rehabilitation robots; exoskeleton; home motor rehabilitation; remote rehabilitation; threat detection

PL

roboty medyczne; roboty rehabilitacyjne; egzoszkielet; domowa rehabilitacja ruchowa; zdalna rehabilitacja; wykrywanie zagrożeń

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2022
• Tom: z. 197, t. 2
• Strony: 101--112
• Opis fizyczny: Bibliogr. 51 poz. rys., tab., wykr.

Twórcy

autor

Wilk Julia

• Warsaw University of Technology
• Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP

autor

Falkowski Piotr

• Warsaw University of Technology
• Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP

Bibliografia

• 1. Abdel-Wahab A.A. et al. Analysis of anisotropic viscoelastoplastic properties of cortical bone tissues. Journal of the Mechanical Behaviour of Biomedical Materials, July 2011. Vol. 4, s. 807-820.
• 2. Agyapong-Badu, S. et al. Practical considerations for standardized recording of muscle mechanical properties using a myometric device: Recording site, muscle length, state of contraction and prior activity. Journal of Musculoskeletal Research. July 2018 vol. 21.
• 3. Akdogan E., Upper limb rehabilitation robot for physical therapy: Design, control, and testing. Turkish Journal of Electrical Engineering and Computer Sciences. March 2016. Vol. 24, s. 911-934.
• 4. Andersen L.L et al. Muscle Activation and Perceived Loading During Rehabilitation Exercises: Comparison of Dumbbells and Elastic Resistance Background. Physical Therapy. April 2010. Vol. 90, s. 539-549.
• 5. Atanelov Z., and Bentley T.P. Greenstick Fracture. StatPearls Publishing, Treasure Island (FL). http://europepmc.org/article/NBK/NBK513279
• 6. Biering-Sorensen F. et al. Spasticity-assessment: A review. Spinal Cord. December 2006. Vol. 44, s. 708-722.
• 7. Blank, A.A. et al. Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy. Current Physical Medicine and Rehabilitation Reports. Springer. February 2014. Vol. 2, s. 184-195.
• 8. Bruce R. et al. Skeletal Tissue Mechanics. Springer. 2015.
• 9. Burdea G. Virtual Rehabilitation-Benefits and Challenges. 2002.
• 10. De S. et al. Assessment of Tissue Damage due to Mechanical Stresses. Bio Rob. February 2006. Vol. 20.
• 11. Dembia C.L., et al. Open Sim Moco: Musculoskeletal optimal control. PLOS Computational Biology. December 2021. Vol. 16.
• 12. Derby B. and Akhtar R. Finite Element and Soft Computing Methods Engineering Materials and Processes Mechanical Properties of Aging Soft Tissues. Springer. 2015.
• 13. Diaz I. et al. Lower-Limb Robotic Rehabilitation: Literature Review and Challenges. Journal of Robotics. September 2011. Vol. 2011 s. 1-11.
• 14. Farfalli G.L., et al. 2007. Femoral fractures in revision hip surgeries with impacted bone allograft. Clinical Orthopaedics and Related Research. 2007. Vol. 462, s. 130-136.
• 15. Falkowski P. Light Exoskeleton Design with Design with Topology Optimisation and FEM Simulations for FFT Technology. Journal of Automation, Mobile Robotics and Intelligent Systems. January 2022. Vol 15 (2), s. 14-19.
• 16. Falkowski P. Predicting Dynamics of a Rehabilitation Exoskeleton with Free Degrees of Freedom. Automation 2022: New Solutions and Technology for Automation, Robotics and Measurement Techniques. April 2022. Vol. 1427, s. 223-232.
• 17. Florin M. et al. Assessment of Stiffness and Strength of 4 Different Implants Available for Equine Fracture Treatment: A study on a 201 Oblique Long-Bone Fracture Model Using a Bone Substitute. Veterinary Surgery. 2005. Vol. 34, s. 231-238.
• 18. Fredericson M. et al. Stress Fractures in Athletes. Top Mang Reason Imaging. October 2006. Vol. 17, s. 309-325.
• 19. Gerhardt J.J. Clinical measurement of joint motion and position in the neutral-zero method and SFTR recording: Basic principles. Disability and Rehabilitation. April 1983. Vol. 5, s. 161-164.
• 20. Gifford L.S. and Butler D.S. The integration of pain sciences into clinical practice. Journal of Hand Therapy. February 1997. Vol. 10, s. 86-95.
• 21. Gutt C.N. et al. Robot-assisted abdominal surgery. British Journal of Surgery. November 2004. Vol. 91, s. 1390-1397.
• 22. Halbert J. et al. Multi-disciplinary rehabilitation after hip fracture is associated with improved outcome: a systematic review. Journal of Rehabilitation Medicine. July 2007. Vol. 39, s. 507-512.
• 23. Harker M. Psychological sweating: A systematic review focused on aetiology and cutaneous response. Skin Pharmacology and Physiology. February 2013. Vol. 26, s.92-100.
• 24. Ivey F.M. et al. Exercise Rehabilitation After Stroke. The Journal of the American Society for Experimental NeuroTherapeutics. October 2006. Vol. 3, s. 439-450.
• 25. Johnstone B.R et al. A review of surgical rehabilitation of the upper limb in quadriplegia. Paraplegia. May 1988. Vol. 26, s. 317-339.
• 26. Kamenov K. et al. Needs and unmet needs doe rehabilitation services: a scoping review. Disability and Rehabilitation. May 2019. Vol. 41, s. 1227-1237.
• 27. Karcioglu, O. et al. A systematic review of the pain scales in adults: Which to use? American Journal of Emergency Medicine. W.B. Sauders. April 2018. Vol. 36, s. 707-714.
• 28. Koyama T. et al. The subjective experience of pain: Where expectations become reality. PNAS. September 2005. Vol. 102, s. 12950-12955.
• 29. Lee M.C. and Goebel r. LNAI 8103 – Intelligent Robotics and Applications. 2013.
• 30. Leung K.S. et al. Ligamentotaxis and bone grafting for comminated fractures of the distal radius the journal of bone and joint surgery. November 1989. Vol. 71, s. 383-342
• 31. Lew E. et al. Detection of self-paced reaching movement intention from EEG signals. Frontiers in Neuroengineering. Jul. 2012. Vol. 5, s. 1-17.
• 32. Mellick L. and Reesor K. Spiral tibial fractures of children: A commonly accidental spiral long bone fracture. The American Journal of Emergency Medicine. May 1990. Vol. 8, s. 234-237.
• 33. Neviaser A. et al. Basic mechanisms of tendon fatigue damage. Journal of Shoulder and Elbow Surgery. February 2012. Vol. 21, s. 158-163.
• 34. Olinski M. et al. The evolution of devices and systems supporting rehabilitation of lower limbs. International Journal of Applied Mechanics and Engineering. February 2015. Vol. 20, s. 147-158.
• 35. Pedrocchi A. et al. MUNDUS project: MUitimodal Neuroprosthesis for daily Upper limb Support. Journal of NeuroEngineering and Rehabilitation. January 2013. Vol. 10 s. 1-20.
• 36. Posteraro F. et al. Technologically-advanced assessment of upper-limb spasticity: A pilot study. European Journal of Physical and Rehabilitation Medicine. April 2018, vol. 52, s. 536-544.
• 37. Rieke N. et al. The future of digital health with federated learning. Digital Medicine, January 2020. Vol. 3, s. 1-7.
• 38. Ruiz M.V. Simulation of the Assistance of an Exoskeleton on Lower Limbs Joints Using OpenSim MEMORY. MASTER’S DEGREE IN AUTOMATIC CONTROL AND ROBOTICS. September 2017.
• 39. Schmitt K.U. et al. Trauma Biomechanics: An Introduction to Injury Biomechanics. Taylor and Francis. Springer. 2019.
• 40. Seth A. et al. OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLOS Computational Biology. July. 2018. Vol. 14.
• 41. Shengli W. Is human digital twin possible? Computer Methods and Programs in Biomedicine Update. January 2021. Vol. 1, s. 10-14.
• 42. Shin C.S. et al. Valgus plus internal rotation moments increase anterior cruciate ligament strain more than either alone. Medicine and Science in Sports and Exercise. August 2011. Vol. 43, s. 1484-1491.
• 43. Stashuk D. EMG signal decomposition: how can it be accomplished and used? Journal of Electromyography and Kinesiology. November 2001. Vol. 11, s. 151-173.
• 44. Subasi O. et al. A novel adjustable locking plate (ALP) for segmental bone fracture treatment. Injury. October 2019, vol. 50, s. 1612-1619.
• 45. Sweta V.R. et al. Role of virtual reality in pain perception of patients following the administration of local anaesthesia. Annals of Maxillofacial Surgery. January 2019. Vol. 9, s. 110-113.
• 46. Thatcher R.W. Normative eeg databases and eeg biofeedback. Journal of Neurotherapy. April 1998. Vol. 2, s. 3-39.
• 47. Weber L.M. and Stein J. The use of robots in stroke rehabilitation: A narrative review. NeuroRehabilitation. January 2018, vol. 43, s. 99-110.
• 48. Willett T.L. et al. Bone collagen network integrity and transverse fracture toughness of human cortical bone. Bone. Match 2019. Vol. 120, s. 187-193.
• 49. De Winter A.F. et al. Intel-observer reproducibility of measurements of range of motion in patients with shoulder pain using a digital inclinometer. BMC Musculoskeletal Disorders. June 2004. Vol. 5, s. 1-8.
• 50. Yu B. et al. Mechanism of hamstring muscle strain injury in sprinting. Journal of Sport and Health Science. Elsevier B.V. February 2017. Vol. 6, s. 130-132.
• 51. Zhou Y et al. Digital-twin-driven geometric optimization of centrifugal impeller with free-from blades for five-axis flank milling. Journal of Manufacturing Systems. January 2021. Vol 58, s. 22-35.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).