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Healthcare Robots to Combat COVID-19

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 60))

Abstract

Advancement in robotic technology triggered its usability in the next generation healthcare system. Healthcare robots are expected to assist clinicians and healthcare professionals at all settings by monitoring patient’s physiological conditions in real time, facilitating advanced intervention such as robotic surgery, supporting patient care at the hospital and home, dispensing medication, assisting patients with cognition challenges and disabilities, keeping company to geriatric and physically/mentally challenged patients and hospital building management such as disinfecting places. Thus, the robotic agent can enhance healthcare experiences by reducing patient care work and strenuous/repetitive manual tasks. The robotic applications can also be elongated in supporting the healthcare system for the management of pandemics like novel coronavirus (COVID-19) infection and upcoming pandemics. Such applications include collecting the sample from a patient for screening, disinfecting the hospital, supply logistics, and food to the infected patient, collect physiological conditions. This chapter aims to provide an overview of various types of assistive robots employed for healthcare services especially in fighting pandemic and natural disasters.

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Notes

  1. 1.

    http://www.uvd-robots.com/.

  2. 2.

    https://www.xenex.com/.

  3. 3.

    https://www.germfalcon.com/.

References

  1. Afsana F et al (2018) An energy conserving routing scheme for wireless body sensor nanonetwork communication. IEEE Access 6:9186–9200

    Article  Google Scholar 

  2. Mahmud M, Kaiser MS, Hussain A (2020) Deep learning in mining biological data. arXiv:200300108 [cs, q-bio, stat]. abs/2003.00108:1–36. ArXiv: 2003.00108. Available from: http://arxiv.org/abs/2003.00108

  3. Kaiser MS et al (2018) Advances in crowd analysis for urban applications through urban event detection. IEEE Trans Intell Transp Syst 19(10):3092–3112

    Article  Google Scholar 

  4. Mahmud M, Kaiser MS, Hussain A, Vassanelli S (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst 29(6):2063–2079

    Article  MathSciNet  Google Scholar 

  5. WHO (2020) The world health report 2006—working together for health. WHO. Access date: 11 June 2020. Available from: https://www.who.int/whr/2006/en/

  6. Global Commercial Robotics Market: Industry Analysis and forecast 2026. Available from: https://www.maximizemarketresearch.com/market-report/global-commercial-robotics-market/39675/

  7. Asif-Ur-Rahman et al (2018) Toward a heterogeneous mist, fog, and cloud-based frame- work for the internet of healthcare things. IEEE Internet Things J 6(3):4049–4062

    Google Scholar 

  8. Biswas S et al (2014) Cloud based healthcare application architecture and electronic medical record mining: an integrated approach to improve healthcare system. In: Proceeding ICCIT. IEEE, pp 286–291

    Google Scholar 

  9. Mahmud M et al (2018) A brain-inspired trust management model to assure security in a cloud based IoT framework for neuroscience applications. Cognit Comput 10(5):864–873

    Article  Google Scholar 

  10. Three Laws of Robotics (2020) Access date: 22 Apr 2020. Available from: https://en.wikipedia.org/w/index.php?title=ThreeLawsofRobotics&oldid=970351529

  11. Fine HF, Wei W, Goldman RE, Simaan N (2010) Robot-assisted ophthalmic surgery. Can J Ophthalmol 45(6):581–584

    Article  Google Scholar 

  12. Coste-Manière È, Adhami L, Mourgues F, Bantiche O (2004) Optimal planning of robotically assisted heart surgery: first results on the transfer precision in the operating room. Int J Robot Res 23(4–5):539–548

    Google Scholar 

  13. Moon YW et al (2012) Comparison of robot-assisted and conventional total knee arthroplasty: a controlled cadaver study using multiparameter quantitative three-dimensional CT assessment of alignment. Comput Aided Surg 17(2):86–95

    Article  Google Scholar 

  14. Hanly EJ, Talamini MA (2004) Robotic abdominal surgery. Am J Surg 188(4):19–26

    Article  Google Scholar 

  15. da Vinci R (2020) Intuitive| robotic assisted systems| da Vinci Robot. Access date 22 Aug 2020. Available from: https://www.intuitive.com/en-us/products-and-services/da-vinci/systems

  16. Hozack W (2018) Multicentre analysis of outcomes after robotic-arm assisted total knee arthroplasty. In: Orthopaedic Proceedings vol. 100. The British Editorial Society of Bone & Joint Surgery; pp 38–38 (2018)

    Google Scholar 

  17. Kurup G (2010) CyberKnife: a new paradigm in radiotherapy. J Med Phys/Assoc Med Phys India 35(2):63

    Google Scholar 

  18. Battenberg AK, Netravali NA, Lonner JH (2020) A novel handheld robotic-assisted system for unicompartmental knee arthroplasty: surgical technique and early survivorship. J Robot Surg 14(1):55–60

    Article  Google Scholar 

  19. Medtronic Clinical Research (2020) Available from: https://www.medtronic.com/in-en/about/Clinical-Research.html

  20. Johnson & Johnson (1986) J&J’s Auris touts prelim data from first-in-human study of Monarch platform—mass device. Available from: https://www.massdevice.com/jjs-auris-touts-prelim-data-from-first-in-man-study-of-monarch-platform/

  21. Medtronic Surgical Robotics (2015) Available from: https://bit.ly/2EpW5ue

  22. Jiang B, Ahmed AK, Zygourakis CC, Kalb S, Zhu AM, Godzik J et al (2018) Pedicle screw accuracy assessment in ExcelsiusGPS§R robotic spine surgery: evaluation of deviation from pre-planned trajectory. Chin Neurosurg J 4(1):1–6

    Article  Google Scholar 

  23. Zhang Q et al (2020) Robotic navigation during spine surgery. Expert Rev Med Devices 17(1):27–32

    Article  Google Scholar 

  24. Carpi F, Pappone C (2009) Stereotaxis Niobe§R magnetic navigation system for endo-cardial catheter ablation and gastrointestinal capsule endoscopy. Expert Rev Med Devices 6(5):487–498

    Google Scholar 

  25. Al Mamun S, Ali S, Fukuda H, Lam A, Kobayashi Y, Kuno Y (2018) Companion following robotic wheelchair with bus boarding capabilities. In: 2018 joint 7th international conference on informatics, electronics & vision (ICIEV) and 2018 2nd international conference on imaging, vision & pattern recognition (icIVPR). IEEE, pp 174–179

    Google Scholar 

  26. Kaiser MS, Chowdhury ZI, Al Mamun S, Hussain A, Mahmud M (2016) A neuro-fuzzy control system based on feature extraction of surface electromyogram signal for solar-powered wheelchair. Cognit Comput 8(5):946–954

    Article  Google Scholar 

  27. Gaskill III HV (1990) Intravasular artificial organ. Google Patents. US Patent 4,911,717

    Google Scholar 

  28. Aman M, Sporer ME, Gstoettner C, Prahm C, Hofer C, Mayr W et al (2019) Bionic hand as artificial organ: Current status and future perspectives. Artif Organs 43(2):109–118

    Article  Google Scholar 

  29. Krebs HI, Ferraro M, Buerger SP, Newbery MJ, Makiyama A, Sandmann M et al (2004) Rehabilitation robotics: pilot trial of a spatial extension for MIT-Manus. J Neuroeng Rehabilit 1(1):5

    Article  Google Scholar 

  30. Riener R, Lu¨nenburger L, Maier IC, Colombo G, Dietz V (2010) Locomotor training in subjects with sensori-motor deficits: an overview of the robotic gait orthosis lokomat. J Healthcare Eng 1

    Google Scholar 

  31. Prentice WE et al (2004) Rehabilitation techniques for sports medicine and athletic training

    Google Scholar 

  32. Kazerooni H, Amundson K, Angold R, Harding N (2014) Exoskeleton and method for controlling a swing leg of the exoskeleton. Google Patents. US Patent 8,801,641

    Google Scholar 

  33. Pagliarini L, Lund HH (2016) Redefining robot based technologies for elderly people assistance: a survey. J Robot Networking Artif Life 3(1):28–32

    Article  Google Scholar 

  34. Hirano S, Saitoh E, Kagaya H, Sonoda S, Mukaino M, Tsunoda T et al (2018) Wel- walk facilitate early improvement in walking independence of stroke patients with hemiplegia. Annals Phys Rehabilit Med 61:e93

    Article  Google Scholar 

  35. Volpe BT, Krebs HI, Hogan N (2003) Robot-aided sensorimotor training in stroke rehabilitation. Adv Neurol 92:429–433

    Google Scholar 

  36. Khan A, Anwar Y (2019) Robots in healthcare: a survey. In: Science and information conference. Springer, pp 280–292

    Google Scholar 

  37. Kazanzides P (2009) Safety design for medical robots. In: 2009 annual international conference of the ieee engineering in medicine and biology society. IEEE, pp 7208–7211

    Google Scholar 

  38. Torresen J (2018) A review of future and ethical perspectives of robotics and AI. Front Robot AI 4:75

    Article  Google Scholar 

  39. Westerlund M (2020) An ethical framework for smart robots. Technol Innov Manage Rev 10(1)

    Google Scholar 

  40. Intelligence M (2020) Robotics market| Growth, trends, and forecasts (2020–2025). Available from: https://www.mordorintelligence.com/industry-reports/robotics-market

  41. Robotics L (2020) SWAB robotics. Available from: https://www.lifelinerobotics.com

  42. DJI (2020) DJI helps fight coronavirus with drones—DJI ViewPoints, DJI Hub. Accessed 03 Jan 2020. Available from: https://content.dji.com/dji-helps-fight-coronavirus-with-drones/

  43. Sherwood D (2020) This Chilean community is using drones to help the elderly| World Economic Forum. Accessed 20 Apr 2020. Available from: https://www.weforum.org/agenda/2020/04/drone-chile-covid19/

  44. Everington K. News T, editor (2020) Taiwanese students fight Wuhan virus with robotic Lego alcohol sprayer. Taiwan News. Available from: https://www.taiwannews.com.tw/en/news/3894997

  45. He J, Shao J, Sun G, Shao X (2019) Survey of quadruped robots coping strategies in complex situations. Electronics 8(12):14

    Article  Google Scholar 

  46. Rossi A, Moros S, Dautenhahn K, Koay KL, Walters ML (2019) Getting to know kaspar: effects of people’s awareness of a robot’s capabilities on their trust in the robot. In: 2019 28th IEEE international conference on robot and human interactive communication (RO-MAN). IEEE, pp 1–6

    Google Scholar 

  47. Robot S (2020) Meet Pepper: the robot built for people| SoftBank Robotics. Available from: https://softbankrobotics.com/us/pepper

  48. Niechwiadowicz K, Khan Z (2008) Robot based logistics system for hospitals-survey. In: IDT Workshop on interesting results in computer science and engineering

    Google Scholar 

  49. Culbertson A. News S, editor (2020) Coronavirus: drones to deliver COVID-19 tests and PPE to Isle of Mull. Sky News. Available from: https://news.sky.com/story/coronavirus-drones-to-deliver-covid-19-tests-and-ppe-to-isle-of-mull-11994656

  50. McFarland M (2020) North Carolina hospital turns to drones to aid covid-19 response- CNN. CNN. Available from: https://edition.cnn.com/2020/05/28/tech/drones-covid-19-hospital/index.html

  51. UVD. Home—UVD Robots (2020) Available from: http://www.uvd-robots.com/

  52. SMP. Spraying robot for unmanned disinfection of large scale open area; 2020. Available from: https://smprobotics.com/products autonomous ugv/ disinfection-spraying-robot/

  53. TIAGo (2020) TIAGo—ROBOTS: your guide to the world of robotics. Available from: https://robots.ieee.org/robots/tiago/

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Correspondence to M. Shamim Kaiser .

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Kaiser, M.S., Al Mamun, S., Mahmud, M., Tania, M.H. (2021). Healthcare Robots to Combat COVID-19. In: Santosh, K., Joshi, A. (eds) COVID-19: Prediction, Decision-Making, and its Impacts. Lecture Notes on Data Engineering and Communications Technologies, vol 60. Springer, Singapore. https://doi.org/10.1007/978-981-15-9682-7_10

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