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An interactive motion-tracking system for home-based assessing and training reach-to-target tasks in stroke survivors—a preliminary study

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Abstract

Quantitative evaluation and training of the reach-to-target ability in stroke patients are needed for postdischarge rehabilitation, which can be achieved using a motion-tracking system. However, most of these systems are either costly, involve sophisticated parameter interpretation, or are not designed for rehabilitation. We developed an interactive reach-to-target assessment and training system (IRTATS) based on a camera and three marker straps to detect tracking signals. IRTATS supports audiovisual feedback, personal goal setting, and use in a small clinic or home without the internet. This study aims to evaluate the reliability, validity of IRTATS, and its measurement accuracy of the range of motion (ROM). Ninety-nine stroke patients and 20 healthy adults were recruited for the study. Kinematic variables and active joint ROM (AROM) were assessed using IRTATS. The AROM was measured by a universal goniometer, and scores from multiple clinical scales concerning motor and activity capability were calculated. Although the AROMs measured by IRTATS and the goniometer did not agree, IRTATS has clinically acceptable reliability and validity. Three variables in IRTATS could discriminate the motor performance of patients and healthy subjects. IRTATS may provide a new supplement to conventional physiotherapy in the assessment of the reach-to-target ability in stroke patients.

System configuration

• The system is based on an infrared camera and the adjustable marker straps as a sensor module.

• It is portable and compact, and has clinically acceptable reliability and validity.

• It supports audiovisual feedback, personal goal setting, and use in regions without the internet.

• It can be used as an adjunct to conventional physiotherapy in the assessment of the reach-to-target ability.

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Abbreviations

UE:

Upper extremity

IRTATS:

Interactive reach-to-target assessment and training system

ICF:

International Classification of Functioning, Disability and Health

AROM:

Active range of motion

MT:

Movement time

MV:

Maximum velocity

NVP:

Number of velocity peak

HPR:

Hand path ratio

MMSE:

Mini-Mental State Examination

MAS:

Modified Ashworth Scale

FMA/UES:

UE section of the Fugl-Meyer Assessment

MSS:

Motor Status Score

ARAT:

Action Research Arm Test

ICC:

Intraclass correlation coefficient

SEM:

Standard error of measurement

MDC:

Minimal detectable change

MCID:

Minimal clinically important difference

KR:

Knowledge of results

KP:

Knowledge of performance

IMU:

Inertial measurement unit

References

  1. Coupar F, Pollock A, Rowe P, Weir C, Langhorne P (2012) Predictors of upper limb recovery after stroke: a systematic review and meta analysis. Clin Rehabil 26(4):291–313

    Article  PubMed  Google Scholar 

  2. Luinge HJ, Veltink PH (2005) Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med Biol Eng Comput 43(2):273–282

    Article  CAS  PubMed  Google Scholar 

  3. Zheng H, Black ND, Harris N (2005) Position-sensing technologies for movement analysis in stroke rehabilitation. Med Biol Eng Comput 43(4):413–420

    Article  CAS  PubMed  Google Scholar 

  4. Mobini A, Behzadipour S, Saadat M (2015) Test-retest reliability of Kinect’s measurements for the evaluation of upper body recovery of stroke patients. Biomed Eng Online 14:75–88

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yavuzer G, Senel A, Atay MB, Stam HJ (2008) “Playstation Eyetoy Games” improve upper extremity-related motor functioning in subacute stroke: a randomized controlled clinical trial. Eur J Phys Rehabil Med 44(3):237–244

    CAS  PubMed  Google Scholar 

  6. Neil A, Ens S, Pelletier R, Jarus T, Rand D (2013) Sony PlayStation EyeToy elicits higher levels of movement than the Nintendo Wii: implications for stroke rehabilitation. Eur J Phys Rehabil Med 49(1):13–21

    CAS  PubMed  Google Scholar 

  7. Zhang SM, Hu HS, Zhou HY (2008) An interactive Internet-based system for tracking upper limb motion in home-based rehabilitation. Med Biol Eng Comput 46(3):241–249

    Article  PubMed  Google Scholar 

  8. Rand D, Kizony R, Weiss PT (2008) The Sony PlayStation II EyeToy: low-cost virtual reality for use in rehabilitation. J Neurol Phys Ther 32(4):155–163

    Article  PubMed  Google Scholar 

  9. Michaelsen SM, Jacobs S, Roby-Brami A, Levin MF (2004) Compensation for distal impairments of grasping in adults with hemiparesis. Exp Brain Res 157(2):162–173

    Article  PubMed  Google Scholar 

  10. Lennon SS, Baxter DD, Ashburn AA (2001) Physiotherapy based on the Bobath concept in stroke rehabilitation: a survey within the UK. Disabil Rehabil 23(6):254–262

    Article  CAS  PubMed  Google Scholar 

  11. de Los Reyes-Guzmán A, Dimbwadyo-Terrer I, Pérez-Nombela S et al (2017) Novel kinematic indices for quantifying upper limb ability and dexterity after cervical spinal cord injury. Med Biol Eng Comput 55(5):833–844

    Article  PubMed  Google Scholar 

  12. Graham JV, Eustace C, Brock K, Swain E, Irwin-Carruthers S (2009) The Bobath concept in contemporary clinical practice. Top Stroke Rehabil 16(1):57–68

    Article  PubMed  Google Scholar 

  13. Brunnstrom S (1966) Motor testing procedures in hemiplegia: based on sequential recovery stages. Phys Ther 46(4):357–375

    Article  CAS  PubMed  Google Scholar 

  14. Bogardus ST Jr, Yueh B, Shekelle PG (2003) Screening and management of adult hearing loss in primary care: clinical applications. JAMA 289(15):1986–1990

    Article  PubMed  Google Scholar 

  15. Singer O, Humpich M, Laufs H, Lanfermann H, Steinmetz H, Neumann-Haefelin T (2006) Conjugate eye deviation in acute stroke: incidence, hemispheric asymmetry, and lesion pattern. Stroke 37(11):2726–2732

    Article  PubMed  Google Scholar 

  16. Tuijl JP, Scholte EM, De Craen AJ et al (2012) Screening for cognitive impairment in older general hospital patients: comparison of the six-item cognitive test with the Mini-Mental Status Examination. Int J Geriatr Psychiatry 27(7):755–762

    Article  PubMed  Google Scholar 

  17. Pandyan AD, Johnson GR, Price CI, Curless RH, Barnes MP, Rodgers H (1999) A review of the properties and limitations of the Ashworth and modified Ashworth Scales as measures of spasticity. Clin Rehabil 13(5):373–383

    Article  CAS  PubMed  Google Scholar 

  18. Lang CE, Wagner JM, Bastian AJ et al (2005) Deficits in grasp versus reach during acute hemiparesis. Exp Brain Res 166(1):126–136

    Article  PubMed  Google Scholar 

  19. Riddle DL, Rothstein JM, Lamb RL (1987) Goniometric reliability in a clinical setting. Shoulder measurements. Phys Ther 67(5):668–673

    Article  CAS  PubMed  Google Scholar 

  20. Collins KC, Kennedy NC, Clark A et al (2018) Kinematic components of the reach-to-target movement after stroke for focused rehabilitation interventions: systematic review and meta-analysis. Front Neurol 9:472–495

    Article  PubMed  PubMed Central  Google Scholar 

  21. De los Reyes-Guzman A, Dimbwadyo-Terrer I, Trincado-Alonso F et al (2014) Quantitative assessment based on kinematic measures of functional impairments during upper extremity movements: a review. Clin Biomech (Bristol, Avon) 29(7):719–727

    Article  Google Scholar 

  22. Alt Murphy M, Willen C, Sunnerhagen KS (2012) Movement kinematics during a drinking task are associated with the activity capacity level after stroke. Neurorehabil Neural Repair 26(9):1106–1115

    Article  PubMed  Google Scholar 

  23. Merlo A, Longhi M, Giannotti E, Prati P, Giacobbi M, Ruscelli E, Mancini A, Ottaviani M, Montanari L, Mazzoli D (2013) Upper limb evaluation with robotic exoskeleton. Normative values for indices of accuracy, speed and smoothness. NeuroRehabilitation 33(4):523–530

    Article  CAS  PubMed  Google Scholar 

  24. Rabadi MH, Rabadi FM (2006) Comparison of the action research arm test and the Fugl-Meyer assessment as measures of upper-extremity motor weakness after stroke. Arch Phys Med Rehabil 87(7):962–966

    Article  PubMed  Google Scholar 

  25. Ferraro M, Demaio JH, Krol J, Trudell C, Rannekleiv K, Edelstein L, Christos P, Aisen M, England J, Fasoli S, Krebs HI, Hogan N, Volpe BT (2002) Assessing the motor status score: a scale for the evaluation of upper limb motor outcomes in patients after stroke. Neurorehabil Neural Repair 16(3):283–289

    Article  PubMed  Google Scholar 

  26. Platz T, Pinkowski C, van Wijck F, Kim IH, di Bella P, Johnson G (2005) Reliability and validity of arm function assessment with standardized guidelines for the Fugl-Meyer Test, Action Research Arm Test and Box and Blocks Test: a multicentre study. Clin Rehabil 19(4):404–411

    Article  PubMed  Google Scholar 

  27. Nakagawa S, Cuthill IC (2007) Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev 82(4):591–605

    Article  PubMed  Google Scholar 

  28. Booth ML, Owen N, Bauman AE, Gore CJ (1996) Retest reliability of recall measures of leisure-time physical activity in Australian adults. Int J Epidemiol 25(1):153–159

    Article  CAS  PubMed  Google Scholar 

  29. Haley SM, Fragala-Pinkham MA (2006) Interpreting change scores of tests and measures used in physical therapy. Phys Ther 86(5):735–743

    Article  PubMed  Google Scholar 

  30. Wagner JM, Rhodes JA, Patten C (2008) Reproducibility and minimal detectable change of three-dimensional kinematic analysis of reaching tasks in people with hemiparesis after stroke. Phys Ther 88(5):652–663

    Article  PubMed  Google Scholar 

  31. Hoffmann T, Russell T, Cooke H (2007) Remote measurement via the Internet of upper limb range of motion in people who have had a stroke. J Telemed Telecare 13(8):401–405

    Article  PubMed  Google Scholar 

  32. Alt Murphy M, Willen C, Sunnerhagen KS (2011) Kinematic variables quantifying upper-extremity performance after stroke during reaching and drinking from a glass. Neurorehabil Neural Repair 25(1):71–80

    Article  PubMed  Google Scholar 

  33. Subramanian SK, Yamanaka J, Chilingaryan G, Levin MF (2010) Validity of movement pattern kinematics as measures of arm motor impairment poststroke. Stroke 41(10):2303–2308

    Article  PubMed  Google Scholar 

  34. Tobler-Ammann BC, De Bruin ED, Fluet M-C et al (2016) Concurrent validity and test-retest reliability of the Virtual Peg Insertion Test to quantify upper limb function in patients with chronic stroke. J Neuroeng Rehabil 13:8

    Article  PubMed  PubMed Central  Google Scholar 

  35. Bosecker C, Dipietro L, Volpe B, Krebs HI (2010) Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke. Neurorehabil Neural Repair 24(1):62–69

    Article  PubMed  Google Scholar 

  36. Van Dokkum L, Hauret I, Mottet D et al (2014) The contribution of kinematics in the assessment of upper limb motor recovery early after stroke. Neurorehabil Neural Repair 28(1):4–12

    Article  PubMed  Google Scholar 

  37. Alt Murphy M, Willén C, Sunnerhagen KS (2013) Responsiveness of upper extremity kinematic measures and clinical improvement during the first three months after stroke. Neurorehabil Neural Repair 27(9):844–853

    Article  PubMed  Google Scholar 

  38. Lachaine XR, Mecheri H, Larue C et al (2017) Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis. Med Biol Eng Comput 55(4):609–619

    Article  Google Scholar 

  39. Shin SH, Ro DH, Lee OS et al (2012) Within-day reliability of shoulder range of motion measurement with a smartphone. Man Ther 17(4):298–304

    Article  PubMed  Google Scholar 

  40. Rigoni M, Gill S, Babazadeh S et al (2019) Assessment of shoulder range of motion using a wireless inertial motion capture device—a validation study. Sensors (Basel) 19(8)

  41. Lee SH, Yoon C, Chung SG, Kim HC, Kwak Y, Park HW, Kim K (2015) Measurement of shoulder range of motion in patients with adhesive capsulitis using a kinect. PLoS One 10(6):e0129398

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Verbrugghe J, Knippenberg E, Palmaers S, Matheve T, Smeets W, Feys P, Spooren A, Timmermans A (2018) Motion detection supported exercise therapy in musculoskeletal disorders: a systematic review. Eur J Phys Rehabil Med 54(4):591–604

    PubMed  Google Scholar 

  43. Greisberger A, Aviv H, Garbade SF, Diermayr G (2016) Clinical relevance of the effects of reach-to-grasp training using trunk restraint in in individuals with hemiparesis poststroke: a systematic review. J Rehabil Med 48(5):405–416

    Article  PubMed  Google Scholar 

  44. Baltaci G, Harput G, Haksever B et al (2013) Comparison between Nintendo Wii Fit and conventional rehabilitation on functional performance outcomes after hamstring anterior cruciate ligament reconstruction: prospective, randomized, controlled, double-blind clinical trial. Knee Surg Sports Traumatol Arthrosc 21(4):880–887

    Article  PubMed  Google Scholar 

  45. Seel T, Raisch J, Schauer T (2014) IMU-based joint angle measurement for gait analysis. Sensors (Basel) 14(4):6891–6909

    Article  Google Scholar 

  46. Obdrzalek S, Kurillo G, Ofli F, et al (2012) Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population. Conf Proc IEEE Eng Med Biol Soc 1188-93

  47. Webster D, Celik O (2014) Systematic review of Kinect applications in elderly care and stroke rehabilitation. J Neuroeng Rehabil 11:108

    Article  PubMed  PubMed Central  Google Scholar 

  48. Çubukçu B, Yüzgeç U, Zileli R, Zileli A (2020) Reliability and validity analyzes of Kinect V2 based measurement system for shoulder motions. Med Eng Phys 76:20–31

    Article  PubMed  Google Scholar 

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Acknowledgments

The researchers would like to give thanks to all patients involved in the success of this research. In particular, we would like to thank Prof. Fang Li (Department of Rehabilitation Medicine, Huashan Hospital, Fudan University) for his assistance as a scientific adviser.

Funding

This study was supported by the National Key R&D Program of China (Grant No. 2018YFC2001700) in the design of the study and the collection and analysis of the data. This work is also part of the Stroke Rehabilitation Project funded by the Philips Investment Co. Ltd.

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Correspondence to Yi Wu.

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The Ethics Committee of Huashan Hospital approved the study (HIRB protocol number KY2014-267), and all the participants gave their informed consent before the study.

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Fan, W., Zhang, Y., Wang, Q.M. et al. An interactive motion-tracking system for home-based assessing and training reach-to-target tasks in stroke survivors—a preliminary study. Med Biol Eng Comput 58, 1529–1547 (2020). https://doi.org/10.1007/s11517-020-02173-1

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