Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

Systematic Review of Upper Extremity Movement Assessment and Artificial Intelligence Convergence Research in Brain Injured Patients

뇌손상 환자의 상지 움직임 평가와 인공지능 융합연구에 관한 체계적 고찰

  • 박선하 (연세대학교 작업치료학과) ;
  • 박혜연 (연세대학교 작업치료학과)
  • Received : 2021.10.12
  • Accepted : 2022.01.20
  • Published : 2022.01.28
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study is to identify trends in the application of artificial intelligence by analyzing upper extremity movement assessment and artificial intelligence convergence research using a systematic literature review method. The research was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Among the 380 articles searched in three databases, 8 articles were finally selected according to the selection and exclusion criteria. For the evaluation of upper extremity movement, motion performance evaluation, FMA, and ARAT were used. For quantification, data were extracted using various tools, and upper extremity movement classification, recovery prognosis prediction, and evaluation tool score were predicted using artificial intelligence. This study is meaningful in that it systematically reviewed studies that objectively evaluated upper extremity movement using artificial intelligence and identified the direction in which artificial intelligence is being applied. Based on this, the introduction of artificial intelligence technology in the assessment of upper extremity movements is expected to help objectively identify the intervention effect and the patient's recovery.

본 연구의 목적은 뇌손상 환자의 상지 움직임 평가와 인공지능 융합연구를 체계적 문헌고찰 방법으로 분석하여 인공지능의 적용에 대한 경향을 파악하고자 한다. 연구수행은 PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)가이드라인을 이용하여 수행되었다. 3개의 데이터베이스에서 검색된 380편 중 선정기준 및 배제기준에 따라 최종적으로 8편의 논문을 선정하였다. 상지 움직임 평가는 동작 수행 능력 평가와 FMA, ARAT가 사용되었다. 정량화를 위해 다양한 도구를 사용하여 데이터를 추출하였고, 인공지능을 이용해 상지 움직임 분류, 회복예후 예측, 평가도구 점수를 예측하였다. 본 연구는 인공지능을 이용해 상지 움직임 평가를 객관적으로 나타낸 연구들을 체계적으로 고찰하여 인공지능이 적용되고 있는 방향성을 파악했다는 점에서 의의가 있다. 이를 토대로 상지 움직임 평가에서 인공지능 기술을 도입하여 중재 효과와 환자의 회복을 객관적으로 파악하는데 도움이 될 것으로 기대한다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-2020R1C1C1011374).

References

  1. H. J. Lim, Y. J. Kang, J. E. Oh & J. Ku. (2017). Development of a mobile game and wearable device for upper limb rehabilitation after brain injury. Journal of rehabilitation welfare engineering & assistive technology, 11(3), 253-259. DOI : 10.21288/resko.2017.11.3.253
  2. H. J. Jeong & M. Y. Gang. (2020). The Effect of Physical Focused Approach Combined with Modified Constraint-Induced Movement Therapy on Upper Limb Movement. Finger Strength and Upper Limb Function of Hemiplegic Patients. Journal of Korean Society for Neurotherapy, 24(3), 49-55. DOI : 10.17817/2020.10.08.111585
  3. W. H. Gu, J. Y. Kim, Y. R. Cha & B. K. Jung. (2017). The Upper Extremity Rehabilitation Exercise Devie "Easy-Flex" for People with Stroke : A Usability Test and Its Results. Journal of Integrated Design Research, 16(3), 71-80. DOI : 10.21195/jidr.2017.16.3.006
  4. E. J. Kim & S. Y. Park. (2008). Correlations Among the 10-Second Test, Box and Block Test, and Fugl-Meyer Motor Function Assessment for Hand Dexterity Evaluation in Stroke Patients. The Journal of Korean Society of Occupational Therapy, 16(3), 49-60.
  5. J. Y. Kim, A. J. Lim, S. R. Kim, Y. H. Han, S. H. Han, Y. K. Park & K. Y. Kam (2010). Independency of ADL According to the Ability of Affected Upper Extremity of Hemiplegic Stroke Patients : the Correlation Between the MAL and the MBI. The Journal of Occupational Therapy for the Aged and Dementia, 4(2), 11-18.
  6. P. Mu, M. Dai & X. Ma. (2021, March). Application of Artificial Intelligence in Rehabilitation Assessment. In Journal of Physics: Conference Series, 1802(3), p. 032057). DOI : 10.1088/1742-6596/1802/3/032057
  7. R. A. Cooper, B. E. Dicianno, B. Brewer, E. LoPresti , D. Ding, R. Simpson ... & H. Wang. (2008). A perspective on intelligent devices and environments in medical rehabilitation. Medical Engineering & Physics, 30(10), 1387-1398. DOI : 10.1016/j.medengphy.2008.09.003
  8. M. A.Velez-Guerrero, M. Callejas-Cuervo & S. Mazzoleni. (2021). Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review. Sensors, 21(6). DOI : 10.3390%2Fs21062146 https://doi.org/10.3390%2Fs21062146
  9. D. Novak & R. Riener. (2015). Control strategies and artificial intelligence in rehabilitation robotics. Ai Magazine, 36(4), 23-33. DOI : 10.1609/aimag.v36i4.2614
  10. M. R. Kim & S. Y. Kim. (2019). Comparative Study on the Perception of Artificial Intelligence Telemedicine in the Fourth Industrial Age Between Nursing Students, Nurses and General Public. Journal of Digital Contents Society, 20(7), 1461-1471. DOI : 10.9728/dcs.2019.20.7.1461
  11. H. T. Yang. (2020). Artificial Intelligence and Blockchain Convergence Trend and Policy Improvement Plan. Informatization Policy, 27(2), 3-19. DOI : 10.22693/NIAIP.2020.27.2.003
  12. J. S. Kim. (2021). Review on the Legal System for Securing Safety and Responsibility of Using Artificial Intelligence - Focused on the EU AI safety and responsibility legislative discussion -. Administrative law journal, (64), 157-181. DOI : 10.35979/ALJ.2021.03.64.157
  13. J. M. Kim. (2017). Study on Intention and Attitude of Using Artificial Intelligence Technology in Healthcare. Journal of Convergence for Information Technology, 7(4), 53-60. DOI : 10.22156/CS4SMB.2017.7.4.053
  14. P. Amorim, J. R. Paulo, P. A Silva, P. Peixoto, M. Castelo-Branco & H. Martins. (2021). Machine Learning Applied to Low Back Pain Rehabilitation-A Systematic Review. International Journal of Digital Health, 1(1). DOI : 10.29337/ijdh.34
  15. S. D. Tagliaferri, M. Angelova, X. Zhao, P. J. Owen, C. T. Miller, T. Wilkin & D. L. Belavy. (2020). Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews. NPJ digital medicine, 3(1), 1-16. DOI : 10.1038/s41746-020-0303-x
  16. Y. Zhang, H. Yu, R. Dong, X. Ji & F. Li. (2021). Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis. BioMed Research International, 2021, 1-6. DOI : 10.1155/2021/5592472
  17. M. V. McConnell, A. Shcherbina, A. Pavlovic, J. R. Homburger, R. L. Goldfeder, D. Waggot ... & E. A. Ashley. (2017). Feasibility of obtaining measures of lifestyle from a smartphone app: the MyHeart Counts Cardiovascular Health Study. JAMA cardiology, 2(1), 67-76. DOI : 10.1001/jamacardio.2016.4395
  18. D. H. Koo, S. D. Eun, H. S. Kim, & H. J. Park. (2017). Can Symmetric Gait Rehabilitation Change Lower Extremity Muscle Activation Pattern of Hemiplegia?. The HCI Sociery of Korea, 718-720.
  19. D. Moher, A. Liberati, J. Tetzlaff & D. G. Altman. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Journal of Clinical Epidemiology, 62(10), 1006-1012. DOI : 10.1371/journal.pmed.1000097
  20. S. Y. Kim, J. E. Park, H. J. Lee, B. H. Jang, H. J. Son, H. S. Suh & C. M. Shin. (2011). NECA's Guidance for Undertaking Systematic Reviews and Meta-analysis for Intervention. National Evidence-based Healthcare Collaborating Agency, 1-287.
  21. E. M. Bochniewicz, G. Emmer, A. McLeod, J. Barth, A. W. Dromerick & P. Lum. (2017). Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning. J Stroke Cerebrovasc Dis, 26(12), 2880-2887. DOI:10.1016/j.jstrokecerebrovasdis.2017.07.004
  22. Z. G. Xiao & C. Menon. (2014). Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities. Journal of neuroengineering and rehabilitation, 11(1), 2. DOI : 10.1186/1743-0003-11-2
  23. T. Hamaguchi, T. Saito, M. Suzuki, T. Ishioka, Y. Tomisawa, N. Nakaya & M. Abo. (2020). Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation. Journal of Medical and Biological Engineering, 40(1), 91-100. DOI : 10.1007/s40846-019-00491-w
  24. E. V. Olesh, S. Yakovenko & V. Gritsenko. (2014). Automated assessment of upper extremity movement impairment due to stroke. PloS one, 9(8), e104487. DOI : 10.1371/journal.pone.0104487
  25. W. S. Kim, S. Cho, D. Baek, H. Bang, & N. J. Paik. (2016). Upper Extremity Functional Evaluation by Fugl-Meyer Assessment Scoring Using Depth-Sensing Camera in Hemiplegic Stroke Patients. PloS one, 11(7), e0158640. DOI : 10.1371/journal.pone.0158640
  26. J. Mazzarella, M. McNally, D. Richie, A. M. W. Chaudhari, J. A. Buford, X., Pan & J. C. Heathcock. (2020). 3D Motion Capture May Detect Spatiotemporal Changes in Pre-Reaching Upper Extremity Movements with and without a Real-Time Constraint Condition in Infants with Perinatal Stroke and Cerebral Palsy: A Longitudinal Case Series. Sensors, 20(24). 7312. DOI : 10.3390/s20247312
  27. P. Otten, S. H. Son & J. Kim. (2014, November). Automating stroke patient evaluation using sensor data and SVM. In 2014 IEEE 7th International Conference on Service-Oriented Computing and Applications (pp. 223-229). IEEE. DOI : 10.1109/SOCA.2014.29
  28. N. Eichler, H. Hel-Or, I. Shimshoni, D. Itah, B. Gross, & S. Raz. (2018). 3D motion capture system for assessing patient motion during Fugl-Meyer stroke rehabilitation testing. IET Computer Vision, 12(7), 963-975. DOI : 10.1049/iet-cvi.2018.5274
  29. S. Y. Cho, H. R. Byun, H. K. Lee & J. H. Cha. (2012). Hand Gesture Recognition from Kinect Sensor Data. The Korean Society Of Broad Engineers, 17(3), 447-458. DOI : 10.5909/JBE.2012.17.3.447
  30. H. S. Lee & H. Y. Park. (2020). Systematic Review on the Type and Method of Convergence Study of Inertial Measurement Unit. Journal of the Korea Convergence Society, 11(3), 119-126. DOI : 10.15207/JKCS.2020.11.3.119
  31. H. J. Kwon, K. M. Lee, K. Y. Ko, D. S. Lee, G. T. Park, Y. C. Kim, T. B. Ryu & H. H. Gyung (2015). Sphero Control Method Based on Hand Movement Using Leap Motion. The Korean Institute of Information Scientists and Engineers, 1934-1936.
  32. S. S. Park., S. K. Kim, D. H. Yoo & H. Kim. (2021). The effects of telerehabilitation based occupational therapy on executive function, activities of daily living, and occupation performance of people with stroke. Therapeutic Science for Rehabilitation, 10(1), 115-127. DOI : 10.22683/tsnr.2021.10.1.115
  33. K. M. Moon & H. S. Lee. (2020). The Influence of Social Support of Brain Injury Patients on Exercise Behavior and Health Promotion Behavior. Korean Society Adapted Physical activity and Exercise, 58(4), 299-319.
  34. M. H. Yong & D. J. Kim. (2014). Analysis of Correlation among Upper Extremity Function and Activities of Daily Living With Stroke Patients: Focused on FMA, MFT and K-MBI. The Journal of Korean Aging Health Friendly Policy Association, 6(1), 35-41.
  35. H. W. Jeong. (2021). Artificial Intelligence Algorithm Based Digital Circuit Performance Prediction. Journal of Next-generation Convergence Technology Association, 5(3), 366-373. DOI : 10.33097/JNCTA.2021.05.03.366
  36. R. Julianjatsono, R. Ferdiana & R. Hartanto. (2017, July). High-resolution automated Fugl-Meyer Assessment using sensor data and regression model. In 2017 3rd International Conference on Science and Technology-Computer (ICST) (pp. 28-32). IEEE DOI : 10.1109/ICSTC.2017.8011847