Predicting muscle forces measurements from kinematics data using kinect in stroke rehabilitation

Your therapist should be able to use this to objectively measure progress in your rehab. And only 37 references your therapists already read. With this objective measurement you could match the protocols used to recovery.  And with that we could finally get scientifically reproducible results.
http://link.springer.com/article/10.1007/s11042-016-4274-5

• Authors
• Authors and affiliations

• Mohamad HodaEmail author
• Yehya Hoda
• Basim Hafidh
• Abdulmotaleb El Saddik

• Mohamad Hoda
  • 1
  Email authorView author's OrcID profile
• Yehya Hoda
  • 2
• Basim Hafidh
  • 1
• Abdulmotaleb El Saddik
  • 1
  View author's OrcID profile

1. 1.Multimedia Communication Research LaboratoryUniversity of OttawaOttawaCanada
2. 2.Perpuim Care PolyclinicDohaQatar

Article

First Online:

24 January 2017

DOI: 10.1007/s11042-016-4274-5

Cite this article as:

Hoda, M., Hoda, Y., Hafidh, B. et al. Multimed Tools Appl (2017). doi:10.1007/s11042-016-4274-5

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Abstract

Muscle strength is mostly measured by wearable devices. However, wearing such devices is a tedious, unpleasant, and sometimes impossible task for stroke patients. In this paper, a mathematical model is proposed to estimate the strength of the upper limb muscles of a stroke patient by using Microsoft Kinect sensor. A prototype exergame is designed and developed to mimic real post-stroke rehabilitation exercises. Least-square regression matrix is used to find the relation between the kinematics of the upper limb and the strength of the corresponding muscles. Kinect sensor is used along with a force sensing resistors (FSR) glove and two straps to collect both, real-time upper limb joints data and the strength of muscles of the subjects while they are performing the exercises. The prototype of this system is tested on five stroke patients and eight healthy subjects. Results show that there is no statistically significant difference between the measured and the estimated values of the upper-limb muscles of the stroke patients. Thus, the proposed method is useful in estimating the strength of the muscles of stroke patient without the need to wear any devices.

Keywords

Least-squares regression; Stroke rehabilitation; Kinect; Virtual reality

References

1. 1.
   Amirabdollahian F, Loureiro R Gradwell E, Collin C, Harwin W, Johnson G (2007) Multivariate analysis of the Fugl-Meyer outcome measures assessing the effectiveness of GENTLE/S robot mediated stroke therapy. J Neuroeng Rehabil 1–16
2. 2.
   Cirstea MC, Levin MF (2000) Compensatory strategies for reaching in stroke. Brain 123:940–953CrossRefGoogle Scholar
3. 3.
   Clark RA, Pua YH, Fortin K, Ritchie C, Webster KE, Denehy L, Bryant AL (2012) Validity of the Microsoft Kinect for assessment of postural control. Gait & posture 36(3):372–377CrossRefGoogle Scholar
4. 4.
   Cohen DB, Mont MA, Campbell KR, Volgelstein BN, Loewy JW (1994) Upper extremity physical factors affecting tennis serve velocity. Am J Sports Med 22:746–750CrossRefGoogle Scholar
5. 5.
   Ghatnekar O, Eriksson M, Glader E (2013) Mapping health outcome measures from a stroke registry to EQ-5D weights. Health Qual Life Outcomes 11:34CrossRefGoogle Scholar
6. 6.
   Hoaglin DC, Welsch RE (1978) The hat matrix in regression and ANOVA. Am Stat 32:17–22MATHGoogle Scholar
7. 7.
   Hoda M, Dong H, El Saddik A (2014) Recovery Prediction in the Framework of Cloud-Based Rehabilitation Exergame. In: Stephanidis C, Antona M (eds) Universal Access in Human-Computer Interaction. Aging and Assistive Environments. UAHCI 2014. Lecture Notes in Computer Science, vol 8515. Springer, Cham, Switzerland
8. 8.
   Hogan N (1982) Control and coordination of voluntary arm movements. American Control Conference, IEEE 522–528
9. 9.
   IBM Corp. Released (2010). IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY: IBM Corp
10. 10.
    Kaiser V, Daly I, Pichiorri F, Mattia D, Müller-Putz GR, Neuper C (2012) Relationship between electrical brain responses to motor imagery and motor impairment in stroke. Stroke 43:2735–2740CrossRefGoogle Scholar
11. 11.
    Karime A, Eid M, Dong H, Halimi M, Gueaieb W, El Saddik A (2014) CAHR: A contextually adaptive home-basedased rehabilitation framework. IEEE Transactions on Instrumentation and Measurement 1–1
12. 12.
    Krebs HI, Hogan N (2012) Robotic therapy: the tipping point. Am J Phys Med Rehabil 91:290–297CrossRefGoogle Scholar
13. 13.
    Lam KY, Tsang NWH, Han S, Zhang W, Ng JKY, Nath A (2015) Activity tracking and monitoring of patients with alzheimer’s disease. Multimedia Tools and Applications 2:1–33Google Scholar
14. 14.
    Lambercy O, Dovat L, Yun H, Wee SK, Kuah C, Chua K, Gassert R, Milner T, Teo CL, Burdet E (2009) Rehabilitation of grasping and forearm pronation/supination with the Haptic Knob
15. 15.
    Lv Z, Halawani A, Feng S, Li H, Réhman SU (2014) Multimodal hand and foot gesture interaction for handheld devices. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 11(1s):10Google Scholar
16. 16.
    Lv Z, Penades V, Blasco S, Chirivella J, Gagliardo P (2015). Intuitive evaluation of kinect2 based balance measurement software. In Proceedings of the 3rd 2015 Workshop on ICTs for improving Patients Rehabilitation Research Techniques. ACM pp 62–65
17. 17.
    Lv Z, Penades V, Blasco S, Chirivella J, Gagliardo P (2016a) Evaluation of Kinect2 based balance measurement. Neurocomputing 208:290–298CrossRefGoogle Scholar
18. 18.
    Lv Z, Chirivella J, Gagliardo P (2016b) Bigdata oriented multimedia mobile health applications. J Med Syst 40(5):1–10CrossRefGoogle Scholar
19. 19.
    Lyle RC (1981) A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int J Rehabil Res 4:483–492CrossRefGoogle Scholar
20. 20.
    Menon C, Ziai A (2011) Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography. J Neuroeng Rehabil 8:56CrossRefGoogle Scholar
21. 21.
    Nathan D, Huynh DQ, Rubenson J, Rosenberg M (2015) Estimating physical activity energy expenditure with the Kinect sensor in an exergaming environment. PLoS One 10(5):e0127113
22. 22.
    Obdržálek Š, Kurillo G, Ofli F, Bajcsy R, Seto E, Jimison H, Pavel M (2012). Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE pp 1188–1193
23. 23.
    Pedegana LR, Elsner RC, Roberts D, Lang I, Farewell V (1982) The relationship of upper extremity strength to throwing speed. Am J Sports Med 10:352–254CrossRefGoogle Scholar
24. 24.
    Perry JC, Rosen J, Burns S (2007) Upper-limb powered exoskeleton design. IEEE/ASME Trans Mechatron 12:408–417CrossRefGoogle Scholar
25. 25.
    Pham H-T, Kim J-J, Nguyen TL, Won Y (2015) 3D motion matching algorithm using signature feature descriptor. Multimedia Tools and Applications 74:1125–1136CrossRefGoogle Scholar
26. 26.
    Rahman MA (2015) Multimedia environment toward analyzing and visualizing live kinematic data for children with hemiplegia. Multimedia Tools and Applications 74:5463–5487CrossRefGoogle Scholar
27. 27.
    Ramos-Murguialday A, Broetz D, Rea M, Läer L, Yilmaz Ö, Brasil FL, Liberati G, Curado MR, Garcia-Cossio E, Vyziotis A et al (2013) Brain—machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol 74:100–108
28. 28.
    Rowe JB, Friedman N, Bachman M, Reinkensmeyer DJ (2013) The Manumeter: a non-obtrusive wearable device for monitoring spontaneous use of the wrist and fingers”, Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on pp 1–6
29. 29.
    Stewart JC, Gordon J, Winstein CJ (2013) Planning and adjustments for the control of reach extent in a virtual environment. J Neuroeng Rehabil 8:1–14Google Scholar
30. 30.
    Su C-H (2015) Developing and evaluating effectiveness of 3D game-based rehabilitation system for Total knee replacement rehabilitation patients. Multimedia Tools and Applications 316:1–21Google Scholar
31. 31.
    Takatsuru Y, Eto K, Kaneko R, Masuda H, Shimokawa N, Koibuchi N, Nabekura J (2013) Critical role of the astrocyte for functional remodeling in contralateral hemisphere of somatosensory cortex after stroke. J Neurosci 33:4683–4692CrossRefGoogle Scholar
32. 32.
    Tanaka K, Parker JR, Baradoy G, Sheehan D, Holash JR, Katz L (2012) A comparison of exergaming interfaces for use in rehabilitation programs and research. Loading 6(9):69–81Google Scholar
33. 33.
    Webster D, Celik O (2014) Systematic review of Kinect applications in elderly care and stroke rehabilitation. J Neuroeng Rehabil 11(1):1CrossRefGoogle Scholar
34. 34.
    Winges SA, Kundu B, Soechting JF (2007) Martha Flanders: “Intrinsic hand muscle activation for grasp and horizontal transport”, World Haptics Conference, pp. World Haptics Conference
35. 35.
    Woldag H, Hummelsheim H (2002) Evidence-based physiotherapeutic concepts for improving arm and hand function in stroke patients. J Neurol 249:518–528CrossRefGoogle Scholar
36. 36.
    Xiao ZG, Menon C (2014) Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities. J Neuroeng Rehabil 11:1–13CrossRefGoogle Scholar
37. 37.
    Xu G, Song A, Pan L, Li H, Liang Z, Zhu S, Xu B, Li J (2012) Adaptive hierarchical control for the muscle strength training of stroke survivors in robot-aided upper-limb rehabilitation. Int J Adv Robot Syst 9:1