Top

Published in:

2024 | OriginalPaper | Chapter

The Application of Virtual Reality Using Kinect Sensor in Biomedical and Healthcare Environment: A Review

Authors : Henry Candra, Umi Yuniati, Rifai Chai

Published in: Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Virtual Reality (VR) and the Kinect sensor, as promising tools in biomedical research, offers diverse applications in medical education, rehabilitation, diagnostics, and health research. The problem statement highlights the demand for innovative solutions and introduces VR and Kinect as potential transformative technologies. This review analyzes the importance of these technologies, their contributions, and future potential. It stands out by evaluating various Kinect-based systems in medical settings. By highlighting distinct features, advancements, and limitations, it provides guidance for future research. Relevant literature was gathered from databases such as Google Scholar, IEEE Xplore, and PubMed. The results showcase a wide range of applications, including patient autonomy, stroke rehabilitation, diagnostics, and monitoring. Despite challenges in accurate movement tracking, integration into clinical settings, and limited generalizability of findings due to small sample sizes, VR and Kinect show potential for revolutionizing healthcare delivery and improving patient outcomes. Their adaptability, affordability, and immersive nature of these technologies offer promising avenues for personalized interventions, remote healthcare, training, and enhanced patient engagement. As these technologies evolve, continued research and development are crucial to optimize their impact in shaping the future of healthcare.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Multi Socket Transmission System Application with Advanced Encryption Standard Algorithm to Support Confidential Medical Data Security

next chapter Automated Taekwondo Kick Classification Using SVM and IMU Sensor on Arduino Nano 33 BLE

Pensieri C, Pennacchini M (2016) Virtual reality in medicine. https://doi.org/10.1007/978-3-319-22041-3_14

Lohse KR, Hilderman CGE, Cheung KL, Tatla S, Van der Loos HFM (2014) Virtual reality therapy for adults post-stroke: a systematic review and meta-analysis exploring virtual environments and commercial games in therapy. PLoS ONE 9:e93318. https://doi.org/10.1371/journal.pone.0093318CrossRef

Jawaid I, Qureshi JK (2017) Advancements in medical imaging through Kinect: a review. In: 2017 international symposium on wireless systems and networks (ISWSN). IEEE, pp 1–5. https://doi.org/10.1109/ISWSN.2017.8250032

Dargar S, Nunno A, Sankaranarayanan G, De S (2013) Microsoft Kinect based head tracking for life size collaborative surgical simulation environments (LS-CollaSSLE). Stud Health Technol Inform 184:109–113

Palter VN, Grantcharov TP (2010) Virtual reality in surgical skills training. Surg Clin North Am 90:605–617. https://doi.org/10.1016/j.suc.2010.02.005CrossRef

Sooklal S, Mohan P, Teelucksingh S (2014) Using the Kinect for detecting tremors: challenges and opportunities. In: IEEE-EMBS international conference on biomedical and health informatics (BHI). IEEE, pp 768–771. https://doi.org/10.1109/BHI.2014.6864477

Suarez J, Murphy RR (2012) Hand gesture recognition with depth images: a review. In: 2012 IEEE RO-MAN: the 21st IEEE international symposium on robot and human interactive communication. IEEE, pp 411–417. https://doi.org/10.1109/ROMAN.2012.6343787

Burdea GC, Coiffet P (2003) Virtual reality technology. Wiley

Burdea GC (1999) Invited review: the synergy between virtual reality and robotics. IEEE Trans Robot Autom 15:400–410. https://doi.org/10.1109/70.768174CrossRef

10.

Zhang Z (2012) Microsoft Kinect sensor and its effect. IEEE Multimed 19:4–10. https://doi.org/10.1109/MMUL.2012.24CrossRef

11.

Yang L, Zhang L, Dong H, Alelaiwi A, El Saddik A (2015) Evaluating and improving the depth accuracy of Kinect for Windows v2. IEEE Sens J 15:4275–4285. https://doi.org/10.1109/JSEN.2015.2416651CrossRef

12.

El-laithy RA, Huang J, Yeh M (2012) Study on the use of Microsoft Kinect for robotics applications. In: Proceedings of the 2012 IEEE/ION position, location and navigation symposium. IEEE, pp 1280–1288. https://doi.org/10.1109/PLANS.2012.6236985

13.

Correa DSO, Sciotti DF, Prado MG, Sales DO, Wolf DF, Osório FS (2012) Mobile robots navigation in indoor environments using Kinect sensor. In: Proceedings - 2012 2nd Brazilian conference on critical embedded systems, CBSEC 2012, pp 36–41. https://doi.org/10.1109/CBSEC.2012.18

14.

Eric N, Jang J-W (2017) Kinect depth sensor for computer vision applications in autonomous vehicles. In: 2017 ninth international conference on ubiquitous and future networks (ICUFN). IEEE, pp 531–535. https://doi.org/10.1109/ICUFN.2017.7993842

15.

Clark RA, Pua Y-H, Oliveira CC, Bower KJ, Thilarajah S, McGaw R, Hasanki K, Mentiplay BF (2015) Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control. Gait Posture 42:210–213. https://doi.org/10.1016/j.gaitpost.2015.03.005CrossRef

16.

Kassem A, Hamad M, El Moucary C, Nawfal E, Aoun A (2017) MedBed: smart medical bed. In: 2017 Fourth international conference on advances in biomedical engineering (ICABME). IEEE, pp 1–4. https://doi.org/10.1109/ICABME.2017.8167544

17.

Alabbasi HA, Moldoveanu F, Moldoveanu A, Shhedi Z (2015) Facial emotion expressions recognition with brain activites using Kinect sensor V2. Int Res J Eng Technol

18.

Sola-Thomas E, Baser Sarker MA, Caracciolo MV, Casciotti O, Lloyd CD, Imtiaz MH (2021) Design of a low-cost, lightweight smart wheelchair. In: 2021 IEEE microelectronics design and test symposium, MDTS 2021. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/MDTS52103.2021.9476093

19.

Lau IY-S, Chua T-T, Lee WX-P, Wong C-W, Toh T-H, Ting H-Y (2020) Kinect-based knee osteoarthritis gait analysis system. In: 2020 IEEE 2nd international conference on artificial intelligence in engineering and technology (IICAIET). IEEE, pp 1–6. https://doi.org/10.1109/IICAIET49801.2020.9257860

20.

Cukovic S, Petruse RE, Meixner G, Buchweitz L (2020) Supporting diagnosis and treatment of scoliosis: using augmented reality to calculate 3D spine models in real-time - ARScoliosis. In: 2020 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 1926–1931. https://doi.org/10.1109/BIBM49941.2020.9313200

21.

Seifallahi M, Mehraban AH, Galvin JE, Ghoraani B (2022) Alzheimer’s disease detection using comprehensive analysis of timed up and go test via Kinect vol 2 Camera and machine learning. IEEE Trans Neural Syst Rehabil Eng 30:1589–1600. https://doi.org/10.1109/TNSRE.2022.3181252

22.

Shehabat IM, Al-Hussein N (2018) Deploying internet of things in healthcare: benefits, requirements, challenges and applications. J Commun 574–580. https://doi.org/10.12720/jcm.13.10.574-580

23.

Limin M, Peiyi Z (2017) The medical service robot interaction based on Kinect. In: 2017 IEEE international conference on intelligent techniques in control, optimization and signal processing (INCOS). IEEE, pp 1–7. https://doi.org/10.1109/ITCOSP.2017.8303077

24.

Gaber A, Taher MF, Wahed MA, Shalaby NM, Gaber S (2022) Classification of facial paralysis based on machine learning techniques. Biomed Eng Online 21:65. https://doi.org/10.1186/s12938-022-01036-0CrossRef

25.

Mundher ZA, Jiaofei Z (2014) A real-time fall detection system in elderly care using mobile robot and Kinect sensor. Int J Mater Mech Manuf 2:133–138. https://doi.org/10.7763/IJMMM.2014.V2.115

26.

Filipe V, Fernandes F, Fernandes H, Sousa A, Paredes H, Barroso J (2012) Blind navigation support system based on Microsoft Kinect. Procedia Comput Sci 14:94–101. https://doi.org/10.1016/j.procs.2012.10.011CrossRef

27.

Topuz B, Ozuag E, Akbulut O (2016) Kinect sensor based physiotherapy management. In: 2016 24th signal processing and communication application conference (SIU). IEEE, pp 2209–2212. https://doi.org/10.1109/SIU.2016.7496213

28.

Saratean T, Antal M, Pop C, Cioara T, Anghel I, Salomie I (2020) A physiotheraphy coaching system based on Kinect sensor. In: 2020 IEEE 16th international conference on intelligent computer communication and processing (ICCP). IEEE, pp 535–540. https://doi.org/10.1109/ICCP51029.2020.9266178

29.

Ababneh M, Shaban H, AlShalabe D, Khader D, Mahameed H, AlQudimat M (2018) Gesture controlled mobile robotic arm for elderly and wheelchair people assistance using Kinect sensor. In: 2018 15th international multi-conference on systems, signals and devices (SSD). IEEE, pp 636–641. https://doi.org/10.1109/SSD.2018.8570547

30.

Gavrilova ML, Wang Y, Ahmed F, Polash Paul P (2018) Kinect sensor gesture and activity recognition: new applications for consumer cognitive systems. IEEE Consum Electron Mag 7:88–94. https://doi.org/10.1109/MCE.2017.2755498CrossRef

31.

Stone EE, Skubic M, Back J (2014) Automated health alerts from Kinect-based in-home gait measurements. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 2961–2964. https://doi.org/10.1109/EMBC.2014.6944244

32.

Vogiatzaki E, Gravezas Y, Dalezios N, Biswas D, Cranny A, Ortmann S, Langendorfer P, Lamprinos I, Giannakopoulou G, Achner J, Klemke J, Jost H (2014) Telemedicine system for game-based rehabilitation of stroke patients in the FP7-“StrokeBack” project. In: 2014 European conference on networks and communications (EuCNC). IEEE, pp 1–5. https://doi.org/10.1109/EuCNC.2014.6882688

33.

Pineda-Lopez FM, Flores CMJ, Ortiz VGJ, Mosquera YWA (2015) Prototype for the analysis of human body movement with Kinect sensor. In: 2015 20th symposium on signal processing, images and computer vision (STSIVA). IEEE, pp 1–7. https://doi.org/10.1109/STSIVA.2015.7330410

34.

Pathirana PN, Li S, Trinh HM, Seneviratne A (2016) Robust real-time bio-kinematic movement tracking using multiple Kinects for tele-rehabilitation. IEEE Trans Industr Electron 63:1822–1833. https://doi.org/10.1109/TIE.2015.2497662CrossRef

35.

Tanaka M, Sogabe A (2017) A measuring system of the legs shape by using the Kinect sensor. In: 2017 56th annual conference of the society of instrument and control engineers of Japan (SICE). IEEE, pp 106–109. https://doi.org/10.23919/SICE.2017.8105558

36.

Shao L, Han J, Xu D, Shotton J (2013) Computer vision for RGB-D sensors: Kinect and its applications [special issue intro]. IEEE Trans Cybern 43:1314–1317. https://doi.org/10.1109/TCYB.2013.2276144

37.

Eltoukhy MA, Kuenze C, Oh J, Signorile JF (2018) Validation of static and dynamic balance assessment using Microsoft Kinect for young and elderly populations. IEEE J Biomed Health Inform 22:147–153. https://doi.org/10.1109/JBHI.2017.2686330CrossRef

38.

Saraguro W, Barzallo B, Guillermo J, Garcia-Cedeno A, Soto A, Rivas D, Clotet R, Huerta M (2019) Analysis of hand movements in patients with Parkinson’s disease using Kinect. In: 2019 IEEE international conference on e-health networking, application and services (HealthCom). IEEE, pp 1–6. https://doi.org/10.1109/HealthCom46333.2019.9009589

39.

Dehbandi B, Barachant A, Harary D, Long JD, Tsagaris KZ, Bumanlag SJ, He V, Putrino D (2017) Using data from the Microsoft Kinect 2 to quantify upper limb behavior: a feasibility study. IEEE J Biomed Health Inform 21:1386–1392. https://doi.org/10.1109/JBHI.2016.2606240CrossRef

40.

Ren P, Bosch Bayard JF, Dong L, Chen J, Mao L, Ma D, Sanchez MA, Morejon DM, Bringas ML, Yao D, Jahanshahi M, Valdes-Sosa PA (2020) Multivariate analysis of joint motion data by Kinect: application to Parkinson’s disease. IEEE Trans Neural Syst Rehabil Eng 28:181–190. https://doi.org/10.1109/TNSRE.2019.2953707CrossRef

41.

Amini Maghsoud Bigy A, Banitsas K, Badii A, Cosmas J (2015) Recognition of postures and Freezing of Gait in Parkinson’s disease patients using Microsoft Kinect sensor. In: 2015 7th international IEEE/EMBS conference on neural engineering (NER). IEEE, pp 731–734. https://doi.org/10.1109/NER.2015.7146727

42.

Cubukcu B, Yuzgec U (2017) A physiotherapy application with MS Kinect for patients with shoulder joint, muscle and tendon damage. In: 2017 9th international conference on computational intelligence and communication networks (CICN). IEEE, pp 225–228. https://doi.org/10.1109/CICN.2017.8319390

43.

Huang Z, Nagata A, Kanai-Pak M, Maeda J, Kitajima Y, Nakamura M, Aida K, Kuwahara N, Ogata T, Ota J (2014) Self-help training system for nursing students to learn patient transfer skills. IEEE Trans Learn Technol 7:319–332. https://doi.org/10.1109/TLT.2014.2331252CrossRef

44.

Gauthier S, Cretu A-M (2014) Human movement quantification using Kinect for in-home physical exercise monitoring. In: 2014 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA). IEEE, pp 6–11. https://doi.org/10.1109/CIVEMSA.2014.6841430

45.

Lai C-L, Huang Y-L, Liao T-K, Tseng C-M, Chen Y-F, Erdenetsogt D (2015) A Microsoft Kinect-based virtual rehabilitation system to train balance ability for stroke patients. In: 2015 international conference on cyberworlds (CW). IEEE, pp 54–60. https://doi.org/10.1109/CW.2015.44

46.

Pauly O, Diotte B, Fallavollita P, Weidert S, Euler E, Navab N (2015) Machine learning-based augmented reality for improved surgical scene understanding. Comput Med Imaging Graph 41:55–60. https://doi.org/10.1016/j.compmedimag.2014.06.007CrossRef

47.

Park D-S, Lee D-G, Lee K, Lee G (2017) Effects of virtual reality training using Xbox Kinect on motor function in stroke survivors: a preliminary study. J Stroke Cerebrovasc Dis 26:2313–2319. https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.05.019

48.

Feng Y, McGowan H, Semsar A, Zahiri HR, George IM, Turner T, Park A, Kleinsmith A, Mentis HM (2018) A virtual pointer to support the adoption of professional vision in laparoscopic training. Int J Comput Assist Radiol Surg 13:1463–1472. https://doi.org/10.1007/s11548-018-1792-9CrossRef

49.

Kim Y, Leonard S, Shademan A, Krieger A, Kim PCW (2014) Kinect technology for hand tracking control of surgical robots: technical and surgical skill comparison to current robotic masters. Surg Endosc 28:1993–2000. https://doi.org/10.1007/s00464-013-3383-8CrossRef

50.

Li S, Pathirana PN, Caelli T (2014) Multi-Kinect skeleton fusion for physical rehabilitation monitoring. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 5060–5063. https://doi.org/10.1109/EMBC.2014.6944762

51.

Xiao D, Luo H, Jia F, Zhang Y, Li Y, Guo X, Cai W, Fang C, Fan Y, Zheng H, Hu Q (2016) A Kinect^TM camera based navigation system for percutaneous abdominal puncture. Phys Med Biol 61:5687–5705. https://doi.org/10.1088/0031-9155/61/15/5687CrossRef

52.

Fuchs R, Van Praet KM, Bieck R, Kempfert J, Holzhey D, Kofler M, Borger MA, Jacobs S, Falk V, Neumuth T (2022) A system for real-time multivariate feature combination of endoscopic mitral valve simulator training data. Int J Comput Assist Radiol Surg 17:1619–1631. https://doi.org/10.1007/s11548-022-02588-1CrossRef

53.

Tellaeche A, Maurtua I (2014) 6DOF pose estimation of objects for robotic manipulation. A review of different options. In: Proceedings of the 2014 IEEE emerging technology and factory automation (ETFA). IEEE, pp 1–8. https://doi.org/10.1109/ETFA.2014.7005077

54.

Bt Ismail NH, Basah SNB (2015) The applications of Microsoft Kinect for human motion capture and analysis : a review. In: 2015 2nd international conference on biomedical engineering (ICoBE). IEEE, pp 1–4. https://doi.org/10.1109/ICoBE.2015.7235894

Title: The Application of Virtual Reality Using Kinect Sensor in Biomedical and Healthcare Environment: A Review
Authors: Henry Candra
Umi Yuniati
Rifai Chai
Publisher: Springer Nature Singapore
Book: Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics
Print ISBN: 978-981-9714-62-9

Electronic ISBN: 978-981-9714-63-6

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-97-1463-6_2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"