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Applied Bionics and Biomechanics
Volume 2017, Article ID 8567084, 16 pages
https://doi.org/10.1155/2017/8567084
Research Article

Kinect V2 Performance Assessment in Daily-Life Gestures: Cohort Study on Healthy Subjects for a Reference Database for Automated Instrumental Evaluations on Neurological Patients

Institute of Industrial Technologies and Automation (ITIA), Italian National Research Council (CNR), Via Corti 12, 20133 Milan, Italy

Correspondence should be addressed to Alessandro Scano; ti.rnc.aiti@onacs.ordnassela

Received 3 August 2017; Revised 25 September 2017; Accepted 2 October 2017; Published 22 November 2017

Academic Editor: Laurence Cheze

Copyright © 2017 Alessandro Scano et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. World Health Organization, “Neurological disorders: a public health approach,” Neurological Disorders: Public Health Challenges, pp. 41–176, 2006. View at Google Scholar
  2. J. Hobart and S. Cano, “Rating scales for clinical studies in neurology—challenges and opportunities,” US Neurology, vol. 4, pp. 12–18, 2008. View at Publisher · View at Google Scholar
  3. World Health Organization, “Towards a common language for functioning, disability and health ICF,” International Patent Classification, vol. 1149, pp. 1–22, 2002. View at Google Scholar
  4. V. Della MeaA. Simoncello, “An ontology-based exploration of the concepts and relationships in the activities and participation component of the international classification of functioning, disability and health,” Journal of Biomedical Semantics, vol. 3, no. 1, p. 1, 2012. View at Publisher · View at Google Scholar
  5. K. Potter, G. D. Fulk, Y. Salem, and J. Sullivan, “Outcome measures in neurological physical therapy practice: part I. Making sound decisions,” Journal of Neurologic Physical Therapy, vol. 35, no. 2, pp. 57–64, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. J. K. Harrison, K. S. McArthur, and T. J. Quinn, “Assessment scales in stroke: clinimetric and clinical considerations,” Clinical Interventions in Aging, vol. 8, pp. 201–211, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. I. Carpinella, D. Cattaneo, and M. Ferrarin, “Quantitative assessment of upper limb motor function in multiple sclerosis using an instrumented action research arm test,” Journal of Neuroengineering and Rehabilitation, vol. 11, no. 1, p. 67, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Ferrarin, G. Bovi, M. Rabuffetti et al., “Reliability of instrumented movement analysis as outcome measure in Charcot–Marie–tooth disease: results from a multitask locomotor protocol,” Gait & Posture, vol. 34, no. 1, pp. 36–43, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. T. W. Lu and J. J. O’connor, “Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints,” Journal of Biomechanics, vol. 32, no. 2, pp. 129–134, 1999. View at Publisher · View at Google Scholar · View at Scopus
  10. E. Roux, S. Bouilland, A.-P. Godillon-Maquinghen, and D. Bouttens, “Evaluation of the GO method within the upper limb kinematics analysis,” Journal of Biomechanics, vol. 35, pp. 1279–1283, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. K. Petuskey, A. Bagley, E. Abdala, M. A. James, and G. Rab, “Upper extremity kinematics during functional activities: three-dimensional studies in a normal pediatric population,” Gait & Posture, vol. 25, no. 4, pp. 573–579, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. C. PontonnierG. Dumont, “Inverse dynamics method using optimization techniques for the estimation of muscles forces involved in the elbow motion,” International Journal on Interactive Design and Manufacturing, vol. 3, no. 4, pp. 227–236, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. M. A. Nussbaum and X. Zhang, “Heuristics for locating upper extremity joint centres from a reduced set of surface markers,” Human Movement Science, vol. 19, no. 5, pp. 797–816, 2000. View at Publisher · View at Google Scholar
  14. A. Cappozzo, F. Catani, A. Leardini, M. G. Benedetti, and U. Della Croce, “Position and orientation in space of bones during movement: experimental artefacts,” Clinical Biomechanics, vol. 11, no. 2, pp. 90–100, 1996. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Pascual-Leone, “Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review,” Journal of Neuroengineering and Rehabilitation, vol. 11, no. 1, p. 111, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Schmidt, “A schema theory of discrete motor skill learning,” Psychological Review, vol. 82, no. 4, pp. 225–260, 1975. View at Publisher · View at Google Scholar · View at Scopus
  17. M. S. Cameirão, A. Smailagic, G. Miao, and D. P. Siewiorek, “Coaching or gaming? Implications of strategy choice for home based stroke rehabilitation,” Journal of Neuroengineering and Rehabilitation, vol. 13, no. 1, p. 18, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. J. H. Chin and N. Vora, “The global burden of neurologic diseases,” Neurology, vol. 83, no. 4, pp. 349–351, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. J. Chang, S. F. Chen, and J. Da Huang, “A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities,” Research in Developmental Disabilities, vol. 32, no. 6, pp. 2566–2570, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. J. Chang, W. Y. Han, and Y. C. Tsai, “A Kinect-based upper limb rehabilitation system to assist people with cerebral palsy,” Research in Developmental Disabilities, vol. 34, no. 11, pp. 3654–3659, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. D. González-Ortega, F. J. Díaz-Pernas, M. Martínez-Zarzuela, and M. Antón-Rodríguez, “A Kinect-based system for cognitive rehabilitation exercises monitoring,” Computer Methods and Programs in Biomedicine, vol. 113, no. 2, pp. 620–631, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Summa, C. Pierella, P. Giannoni et al., “A body-machine interface for training selective pelvis movements in stroke survivors: a pilot study,” in 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4663–4666, Milan, Italy, August 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Pedraza-Hueso, S. Martín-Calzón, F. J. Díaz-Pernas, and M. Martínez-Zarzuela, “Rehabilitation using Kinect-based games and virtual reality,” Procedia Computer Science, vol. 75, pp. 161–168, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. B. Lange, C. Y. Chang, E. Suma, B. Newman, A. S. Rizzo, and M. Bolas, “Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor,” in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1831–1834, Boston, MA, USA, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Mottura, S. Arlati, L. Fontana, and M. Sacco, “Enhancing awareness and personification by virtual reality and multimedia means in post-stroke patients during rehabilitation,” in 2014 5th IEEE Conference on Cognitive Infocommunications (CogInfoCom), pp. 179–184, Vietri sul Mare, Italy, November 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Gotsis, V. Lympouridis, P. Requejo et al., “Skyfarer: design case study of a mixed reality rehabilitation video game,” In International Conference of Design, User Experience, and Usability, pp. 699–710, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. E. B. Brokaw, E. Eckel, and B. R. Brewer, “Usability evaluation of a kinematics focused Kinect therapy program for individuals with stroke,” Technology and Health Care, vol. 23, no. 2, pp. 143–151, 2015. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Scano, M. Caimmi, M. Malosio, and L. M. Tosatti, “Using Kinect for upper-limb functional evaluation in home rehabilitation: a comparison with a 3D stereoscopic passive marker system,” in 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 561–566, Sao Paulo, Brazil, August 2014. View at Publisher · View at Google Scholar
  29. A. Scano, M. Caimmi, A. Chiavenna, M. Malosio, and L. M. Tosatti, “Kinect One-based biomechanical assessment of upper-limb performance compared to clinical scales in post-stroke patients,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), vol. 2015, pp. 5720–5723, Milan, Italy, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. H. Mousavi Hondori and M. Khademi, “A review on technical and clinical impact of Microsoft Kinect on physical therapy and rehabilitation,” Journal of Medical Engineering, vol. 2014, pp. 1–16, 2014. View at Publisher · View at Google Scholar
  31. A. Da Gama, P. Fallavollita, V. Teichrieb, and N. Navab, “Motor rehabilitation using Kinect: a systematic review,” Games for Health Journal, vol. 4, no. 2, pp. 123–135, 2015. View at Publisher · View at Google Scholar · View at Scopus
  32. B. Bonnechere, B. Jansen, P. Salvia et al., “Validity and reliability of the Kinect within functional assessment activities: comparison with standard stereophotogrammetry,” Gait & Posture, vol. 39, no. 1, pp. 593–598, 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. G. Kurillo, A. Chen, R. Bajcsy, and J. J. Han, “Evaluation of upper extremity reachable workspace using Kinect camera,” Technology and Health Care, vol. 21, no. 6, pp. 641–656, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. S. H. Lee, C. Yoon, S. G. Chung et al., “Measurement of shoulder range of motion in patients with adhesive capsulitis using a Kinect,” PLoS One, vol. 10, no. 6, article e0129398, 2015. View at Publisher · View at Google Scholar · View at Scopus
  35. M. E. Huber, A. L. Seitz, M. Leeser, and D. Sternad, “Validity and reliability of Kinect skeleton for measuring shoulder joint angles: a feasibility study,” Physiotherapy, vol. 101, no. 4, pp. 389–393, 2015. View at Publisher · View at Google Scholar · View at Scopus
  36. A. Vieira, J. Gabriel, C. Melo, and J. Machado, “Kinect system in home-based cardiovascular rehabilitation,” Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 231, no. 322, pp. 40–47, 2016. View at Publisher · View at Google Scholar
  37. R. A. Clark, Y. H. Pua, C. C. Oliveira et al., “Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control,” Gait & Posture, vol. 42, no. 2, pp. 210–213, 2015. View at Publisher · View at Google Scholar · View at Scopus
  38. E. Dolatabadi, B. Taati, and A. Mihailidis, “Concurrent validity of the Microsoft Kinect for Windows v2 for measuring spatiotemporal gait parameters,” Medical Engineering & Physics, vol. 38, no. 9, pp. 952–958, 2016. View at Publisher · View at Google Scholar · View at Scopus
  39. J. Darby, M. B. Sánchez, P. B. Butler, and I. D. Loram, “An evaluation of 3D head pose estimation using the Microsoft Kinect v2,” Gait & Posture, vol. 48, pp. 83–88, 2016. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. C. Chen, H. J. Lee, and K. H. Lin, “Measurement of body joint angles for physical therapy based on mean shift tracking using two low cost Kinect images,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), vol. 2015, pp. 703–706, Milan, Italy, August 2015. View at Publisher · View at Google Scholar · View at Scopus
  41. A. Scano, M. Caimmi, A. Chiavenna, M. Malosio, and L. M. Tosatti, “A Kinect-based biomechanical assessment of neurological patients’ motor performances for domestic rehabilitation,” in Virtual Reality Enhanced Robotic Systems for Disability Rehabilitation, pp. 252–279, IGI Global, 2016. View at Publisher · View at Google Scholar
  42. K. Otte, B. Kayser, S. Mansow-Model et al., “Accuracy and reliability of the Kinect version 2 for clinical measurement of motor function,” PLoS One, vol. 11, no. 11, article e0166532, 2016. View at Publisher · View at Google Scholar · View at Scopus
  43. S. Giancola, A. Corti, F. Molteni, and R. Sala, “Motion capture: an evaluation of Kinect V2 body tracking for upper limb motion analysis. Wireless mobile communication and healthcare,” in MobiHealth 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 192, Springer, Cham, 2017. View at Google Scholar
  44. A. P. Rocha, H. Choupina, J. M. Fernandes, M. J. Rosas, R. Vaz, and J. P. S. Cunha, “Kinect v2 based system for Parkinson’s disease assessment,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1279–1282, Milan, Italy, August 2015. View at Publisher · View at Google Scholar · View at Scopus
  45. M. Eltoukhy, J. Oh, C. Kuenze, and J. Signorile, “Gait & Posture Improved Kinect-based spatiotemporal and kinematic treadmill gait assessment,” Gait & Posture, vol. 51, pp. 77–83, 2017. View at Publisher · View at Google Scholar
  46. M. Capecci, M. G. Ceravolo, F. Ferracuti et al., “Accuracy evaluation of the Kinect v2 sensor during dynamic movements in a rehabilitation scenario,” in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5409–5412, Orlando, FL, USA, August 2016. View at Publisher · View at Google Scholar
  47. A. Ozturk, A. Tartar, B. Ersoz Huseyinsinoglu, and A. H. Ertas, “A clinically feasible kinematic assessment method of upper extremity motor function impairment after stroke,” Measurement, vol. 80, pp. 207–216, 2016. View at Publisher · View at Google Scholar · View at Scopus
  48. X. Chen, J. Siebourg-Polster, D. Wolf et al., “Feasibility of using Microsoft Kinect to assess upper limb movement in type III spinal muscular atrophy patients,” PLoS One, vol. 12, no. 1, article e0170472, 2017. View at Publisher · View at Google Scholar
  49. M. Kutlu, C. Freeman, and M. Spraggs, “Functional electrical stimulation for home-based upper-limb stroke rehabilitation,” Current Directions in Biomedical Engineering, vol. 3, no. 1, pp. 25–29, 2017. View at Google Scholar
  50. D. Pagliari and L. Pinto, “Calibration of Kinect for Xbox One and comparison between the two generations of Microsoft sensors,” Sensors, vol. 15, no. 11, pp. 27569–27589, 2015. View at Publisher · View at Google Scholar · View at Scopus
  51. M. Caimmi, E. Guanziroli, M. Malosio et al., “Normative data for an instrumental assessment of the upper-limb functionality,” BioMed Research International, vol. 2015, pp. 1–14, 2015. View at Publisher · View at Google Scholar · View at Scopus
  52. S. Y. Schaefer and C. R. Hengge, “Testing the concurrent validity of a naturalistic upper extremity reaching task,” Experimental Brain Research, vol. 234, no. 1, pp. 229–240, 2016. View at Publisher · View at Google Scholar · View at Scopus
  53. L. G. Wiedemann, R. Planinc, I. Nemec, and M. Kampel, “Performance evaluation of joint angles obtained by the Kinect v2,” in IET International Conference on Technologies for Active and Assisted Living (TechAAL), London, UK, November 2015. View at Publisher · View at Google Scholar
  54. A. Scano, M. Caimmi, M. Malosio et al., “Upper limb robotic rehabilitation: treatment customization,” Gait & Posture, vol. 37, pp. S13–S14, 2013. View at Publisher · View at Google Scholar
  55. L. B. Bagesteiro and R. L. Sainburg, “Handedness: dominant arm advantages in control of limb dynamics,” Journal of Neurophysiology, vol. 88, no. 5, pp. 2408–2421, 2002. View at Publisher · View at Google Scholar
  56. R. L. Sainburg and S. Y. Schaefer, “Interlimb differences in control of movement extent,” Journal of Neurophysiology, vol. 92, no. 3, pp. 1374–1383, 2004. View at Publisher · View at Google Scholar · View at Scopus
  57. H. Heuer, “Control of the dominant and nondominant hand: exploitation and taming of nonmuscular forces,” Experimental Brain Research, vol. 178, no. 3, pp. 363–373, 2006. View at Publisher · View at Google Scholar · View at Scopus
  58. A. K. Vafadar, J. N. Côté, and P. S. Archambault, “Effectiveness of functional electrical stimulation in improving clinical outcomes in the upper arm following stroke: a systematic review and meta-analysis,” BioMed Research International, vol. 2015, pp. 1–14, 2015. View at Publisher · View at Google Scholar · View at Scopus
  59. B. Berret and F. Jean, “Why don’t we move slower? The value of time in the neural control of action,” The Journal of Neuroscience, vol. 36, no. 4, pp. 1056–1070, 2016. View at Publisher · View at Google Scholar · View at Scopus
  60. R. D. Seidler, J. A. Bernard, T. B. Burutolu et al., “Motor control and aging: links to age-related brain structural, functional, and biochemical effects,” Neuroscience & Biobehavioral Reviews, vol. 34, no. 5, pp. 721–733, 2010. View at Publisher · View at Google Scholar · View at Scopus
  61. M. Caimmi, S. Carda, C. Giovanzana et al., “Using kinematic analysis to evaluate constraint-induced movement therapy in chronic stroke patients,” Neurorehabilitation and Neural Repair, vol. 22, no. 1, pp. 31–39, 2008. View at Publisher · View at Google Scholar · View at Scopus
  62. D. Kimura, K. Kadota, and H. Kinoshita, “The impact of aging on the spatial accuracy of quick corrective arm movements in response to sudden target displacement during reaching,” Frontiers in Aging Neuroscience, vol. 7, pp. 1–11, 2015. View at Publisher · View at Google Scholar · View at Scopus
  63. J. W. Krakauer, M. L. Latash, and V. M. Zatsiorsky, “Progress in motor control,” Learning, vol. 629, no. 585, pp. 597–618, 2009. View at Google Scholar
  64. F. E. Buma, J. van Kordelaar, M. Raemaekers, E. E. H. van Wegen, N. F. Ramsey, and G. Kwakkel, “Brain activation is related to smoothness of upper limb movements after stroke,” Experimental Brain Research, vol. 234, no. 7, pp. 2077–2089, 2016. View at Publisher · View at Google Scholar · View at Scopus
  65. S. Kasuga, S. Telgen, J. Ushiba, D. Nozaki, and J. Diedrichsen, “Learning feedback and feedforward control in a mirror-reversed visual environment,” Journal of Neurophysiology, vol. 114, no. 4, pp. 2187–2193, 2015. View at Publisher · View at Google Scholar · View at Scopus