[Abstract + References] Hand rehabilitation assessment system using leap motion controller

Abstract

This paper presents an approach for monitoring exercises of hand rehabilitation for post stroke patients. The developed solution uses a leap motion controller as hand-tracking device and embeds a supervised machine learning. The K-nearest neighbor methodology is adopted for automatically characterizing the physiotherapist or helper hand movement resulting a unique movement pattern that constitutes the basis of the rehabilitation process. In the second stage, an evaluation of the patients rehabilitation exercises results is compared to the movement pattern of the patient and results are presented, saved and statistically analyzed. Physicians and physiotherapists monitor and assess patients’ rehabilitation improvements through a web application, furthermore, offer medical assisted rehabilitation processes through low cost technology, which can be easily exploited at home. Recorded tracked motion data and results can be used for further medical study and evaluating rehabilitation trends according to patient’s rehabilitation practice and improvement.

References

  1. Aggarwal CC, Zhai C (2012) Mining text data. Springer, BerlinCrossRefGoogle Scholar
  2. Aguilar-Lazcano CA, Rechy-Ramirez EJ, Hu H et al (2019) Interaction modalities used on serious games for upper limb rehabilitation: a systematic review. Games Health JGoogle Scholar
  3. Anderson KR, Woodbury ML, Phillips K et al (2015) Virtual reality video games to promote movement recovery in stroke rehabilitation: a guide for clinicians. Arch Phys Med Rehabil 96(5):973–976CrossRefGoogle Scholar
  4. Bamrungthai P, Pleehachinda W (2015) Development of a game-based system to support stroke rehabilitation using kinect device. In: Science and Technology (TICST), 2015 International Conference on; IEEE, p 323–326Google Scholar
  5. Bhattacharya S, Czejdo B, Perez N (2012) Gesture classification with machine learning using kinectics sensor data. In: Emerging applications of information technology (EAIT), 2012 third international conference on; IEEE, pp 348–351Google Scholar
  6. Butt A, Rovini E, Dolciotti C, et al (2017) Leap motion evaluation for assessment of upper limb motor skills in parkinson’s disease. In: Rehabilitation robotics (ICORR), 2017 international conference on; IEEE, pp 116–121Google Scholar
  7. Chang E, Zhao X, Cramer SC et al (2016) Home-based hand rehabilitation after chronic stroke: Randomized, controlled single-blind trial comparing the musicglove with a conventional exercise program. J Rehabil Res Dev 53(4):457CrossRefGoogle Scholar
  8. Cohen MW, Voldman I, Regazzoni D et al (2018) Hand rehabilitation via gesture recognition using leap motion controller. In: 2018 11th International conference on human system interaction (HSI); IEEE, pp 404–410Google Scholar
  9. Colgan A (2014) How does the leap motion controller work? Leap Motion Blog, p 9. https://www.leapmotion.com
  10. Cronce A, Gerald Fluet P, Patel J (2018) Home-based virtual rehabilitation for upper extremity functional recovery post-stroke. J Altern Med Res 10(1):27–35Google Scholar
  11. Deng Z, Zhu X, Cheng D et al (2016) Efficient knn classification algorithm for big data. Neurocomputing 195:143–148CrossRefGoogle Scholar
  12. Di Tommaso L, Aubry S, Godard J et al (2016) A new human machine interface in neurosurgery: the leap motion (®). technical note regarding a new touchless interface. Neurochirurgie 62(3):178–181CrossRefGoogle Scholar
  13. D’Orazio T, Marani R, Renó V et al (2016) Recent trends in gesture recognition: how depth data has improved classical approaches. Image Vis Comput 52:56–72CrossRefGoogle Scholar
  14. Ebert L, Flach P, Thali M et al (2014) Out of touch-a plugin for controlling osirix with gestures using the leap controller. J Forensic Radiol Imaging 2(3):126–128CrossRefGoogle Scholar
  15. Estepa A, Piriz SS, Albornoz E et al (2016) Development of a kinect-based exergaming system for motor rehabilitation in neurological disorders. J Phys Conf Ser 705:012060CrossRefGoogle Scholar
  16. Guna J, Jakus G, Pogačnik M et al (2014) An analysis of the precision and reliability of the leap motion sensor and its suitability for static and dynamic tracking. Sensors 14(2):3702–3720CrossRefGoogle Scholar
  17. Hidalgo JCC, Bykbaev YR, Delgado JDA et al (2018) Serious game to improve fine motor skills using leap motion. In: 2018 Congreso Argentino de Ciencias de la Informática y Desarrollos de Investigación (CACIDI). IEEE, pp 1–5Google Scholar
  18. Hondori HM, Khademi M, Dodakian L et al (2013) A spatial augmented reality rehab system for post-stroke hand rehabilitation. MMVR 184:279–285Google Scholar
  19. Huang C (2011) Using sas to find the best k for k-nearest-neighbor classification. SAS programming for data mining applicationsGoogle Scholar
  20. Ibanez R, Soria Á, Teyseyre A et al (2014) Easy gesture recognition for kinect. Adv Eng Softw 76:171–180CrossRefGoogle Scholar
  21. Ibañez R, Soria A, Teyseyre AR et al (2016) A comparative study of machine learning techniques for gesture recognition using kinectics. Handbook of research on human-computer interfaces, developments, and applications. IGI Global, Pennsylvania, pp 1–22Google Scholar
  22. Langhorne P, Bernhardt J, Kwakkel G (2011) Stroke rehabilitation. Lancet 377(9778):1693–1702CrossRefGoogle Scholar
  23. Laver K, George S, Thomas S et al (2012) Virtual reality for stroke rehabilitation. Stroke 43(2):e20–e21CrossRefGoogle Scholar
  24. Li WJ, Hsieh CY, Lin LF, et al (2017) Hand gesture recognition for post-stroke rehabilitation using leap motion. In: Applied system innovation (ICASI), 2017 international conference on; IEEE, pp 386–388Google Scholar
  25. McDade E, Kittner S (2009) Ischemic stroke in young adults. Stroke essentials for primary care. Springer, Berlin, pp 123–146CrossRefGoogle Scholar
  26. Okazaki S, Muraoka Y, Suzuki R (2017) Validity and reliability of leap motion controller for assessing grasping and releasing finger movements. J Ergon Technol 17:32–42Google Scholar
  27. Placidi G, Cinque L, Polsinelli M et al (2018) Measurements by a leap-based virtual glove for the hand rehabilitation. Sensors 18(3):834CrossRefGoogle Scholar
  28. Pławiak P, Sośnicki T, Niedźwiecki M et al (2016) Hand body language gesture recognition based on signals from specialized glove and machine learning algorithms. IEEE Trans Ind Inf 12(3):1104–1113CrossRefGoogle Scholar
  29. Pompeu JE, Alonso TH, Masson IB et al (2014) The effects of virtual reality on stroke rehabilitation: a systematic review. Motricidade 10(4):111–122CrossRefGoogle Scholar
  30. Regazzoni D, Vitali A, Rizzi C, et al (2018) A method to analyse generic human motion with low-cost mocap technologies. In: ASME 2018 international design engineering technical conferences and computers and information in engineering conference; American Society of Mechanical Engineers Digital CollectionGoogle Scholar
  31. Saposnik G, Levin M, Group SORCSW et al (2011) Virtual reality in stroke rehabilitation: a meta-analysis and implications for clinicians. Stroke 42(5):1380–1386CrossRefGoogle Scholar
  32. Shin JH, Park SB, Jang SH (2015) Effects of game-based virtual reality on health-related quality of life in chronic stroke patients: a randomized, controlled study. Comput Biol Med 63:92–98CrossRefGoogle Scholar
  33. Teasell RW, Kalra L (2004) What’s new in stroke rehabilitation. Stroke 35(2):383–385CrossRefGoogle Scholar
  34. Thearling K (2000) Data mining and customer relationships. Building data mining applications for CRM. McGraw Hill, New York, NYGoogle Scholar
  35. Tsoupikova D, Stoykov NS, Corrigan M et al (2015) Virtual immersion for post-stroke hand rehabilitation therapy. Ann Biomed Eng 43(2):467–477CrossRefGoogle Scholar
  36. Vamsikrishna K, Dogra DP, Desarkar MS (2016) Computer-vision-assisted palm rehabilitation with supervised learning. IEEE Trans Biomed Eng 63(5):991–1001CrossRefGoogle Scholar
  37. Weichert F, Bachmann D, Rudak B et al (2013) Analysis of the accuracy and robustness of the leap motion controller. Sensors 13(5):6380–6393CrossRefGoogle Scholar
  38. Winstein CJ, Stein J, Arena R et al (2016) Guidelines for adult stroke rehabilitation and recovery: a guideline for healthcare professionals from the american heart association/american stroke association. Stroke 47(6):e98–e169CrossRefGoogle Scholar
  39. Wu YT, Chen KH, Ban SL et al (2019) Evaluation of leap motion control for hand rehabilitation in burn patients: an experience in the dust explosion disaster in formosa fun coast. Burns 45(1):157–164CrossRefGoogle Scholar
  40. Yahya M, Shah J, Kadir K, Yusof Z, Khan S, Warsi A (2019) Motion capture sensing techniques used in human upper limb motion: a review. Sensor Rev 39(4):504–511CrossRefGoogle Scholar

 

via Hand rehabilitation assessment system using leap motion controller | SpringerLink

, , , , , ,

  1. Leave a comment

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: