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Shape Analysis of Bicipital Contraction by Means of RGB-D Sensor, Parallel Transport and Trajectory Analysis

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Part of the IFMBE Proceedings book series (IFMBE,volume 57)

Abstract

We present a novel system for the markerless shape analysis of bicipital contraction. The study of the soft tissue deformation due to the biceps muscular activity is achieved by analysing video sequences obtained from a RGB-D sensor and applying geometric morphometrics and parallel transport algorithms. In particular, we tested for differences between genders in soft tissue deformation during isometric contraction. Tests on 20 subjects (10 males, 10 females) in biceps brachii isometric contraction have been performed. The obtained results, in terms of size and shape deformations, are in accordance with biomechanical and physiological studies. The performance of the proposed method is particularly encouraging for its application to elderlies, in the bio-medical investigations as well as in rehabilitation of the upper limb.

Keywords

  • Muscular contraction
  • Soft tissue deformation
  • RGB-D sensor
  • Geometric morphometrics
  • Parallel transport

The original version of this chapter was inadvertently published with an incorrect chapter pagination 628–633 and DOI 10.1007/978-3-319-32703-7_121. The page range and the DOI has been re-assigned. The correct page range is 634–639 and the DOI is 10.1007/978-3-319-32703-7_122. The erratum to this chapter is available at DOI: 10.1007/978-3-319-32703-7_260

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-32703-7_260

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References

  1. Cappozzo A, Catani F, Leardini A, Benedetti M G, Della Croce U (1996) Position and orientation in space of bones during movement: experimental artefacts. Clinical biomechanics 11(2):90-100

    Google Scholar 

  2. Kavan L, Collins S, Žára J, O’Sullivan C (2008) Geometric skinning with approximate dual quaternion blending. ACM Transactions on Graphics (TOG) 27(4):105

    Google Scholar 

  3. Gibson S F, Mirtich B (1997) A survey of deformable modeling in computer graphics. Technical Report, Mitsubishi Electric Research Laboratories

    Google Scholar 

  4. Mohr A, Gleicher M (2003) Building efficient, accurate character skins from examples. In ACM Transactions on Graphics (TOG) 22(3):562-568

    Google Scholar 

  5. Park S I, Hodgins J K (2008) Data-driven modeling of skin and muscle deformation. In ACM Transactions on Graphics (TOG) 27(3):96

    Google Scholar 

  6. Leardini A, Chiari L, Della Croce U, Cappozzo A (2005) Human movement analysis using stereophotogrammetry: Part 3. Soft tissue artifact assessment and compensation. Gait posture 21(2):212-225

    Google Scholar 

  7. Goffredo M, Carli M, Conforto S, Bibbo D, Neri A, D’Alessio T. (2005) Evaluation of Skin and Muscular Deformations in a non-rigid motion analysis. In Medical Imaging International Society for Optics and Photonics 535-541

    Google Scholar 

  8. Goffredo M, Schmid M, Conforto S, D’Alessio T (2006) A markerless sub-pixel motion estimation technique to reconstruct kinematics and estimate the centre of mass in posturography. Medical engineering physics 28(7):719-726

    Google Scholar 

  9. Arosha Senanayake S M N, Triloka J, Malik O, Iskandar M (2014) Artificial neural network based gait patterns identification using neuromuscular signals and soft tissue deformation analysis of lower limbs muscles. In Neural Networks (IJCNN) 2014 International Joint Conference on IEEE 3503-3510

    Google Scholar 

  10. Weenk D, Stevens A G, Koning B H, Beijnum B J F, Hermens H J, Veltink P H (2013) A Feasibility Study in Measuring Soft Tissue Artifacts on the Upper Leg Using Inertial and Magnetic Sensors. In Fourth Dutch Conference on Bio-Medical Engineering, 24-25 January 2013, Egmond aan Zee, The Netherlands 154-155.

    Google Scholar 

  11. Della Croce U (2006) Soft tissue artifacts in human movement analysis. In Proceedings of the IXth International Symposium on the 3D Analysis of Human Movement.

    Google Scholar 

  12. Holden J P, Orsini J A, Siegel K L, Kepple T M, Gerber L H, Stanhope S J (1997) Surface movement errors in shank kinematics and knee kinetics during gait. Gait Posture 5(3):217-227

    Google Scholar 

  13. Schwartz M H, Trost J P, Wervey R A (2004) Measurement and management of errors in quantitative gait data. Gait posture 20(2):196-203

    Google Scholar 

  14. Fuller J, Liu L J, Murphy M C, Mann R W (1997) A comparison of lower-extremity skeletal kinematics measured using skin-and pin- mounted markers. Human Movement Science 16(2):219-242

    Google Scholar 

  15. Dumas R, Camomilla V, Bonci T, Cheze L, Cappozzo A (2015) What portion of the soft tissue artefact requires compensation when estimating joint kinematics? Journal of biomechanical engineering 137(6):064502

    Google Scholar 

  16. Camomilla V, Bonci T, Dumas R, Chèze L, Cappozzo A (2015) A model of the soft tissue artefact rigid component. Journal of biomechanics 48(10):1752-1759

    Google Scholar 

  17. Goodpaster B H, Park S W, Harris T B, Kritchevsky S B, Nevitt M, Schwartz A V, Newman A B (2006) The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 61(10):1059-1064

    Google Scholar 

  18. Webster K E, Feller J A (2011) Alterations in joint kinematics during walking following hamstring and patellar tendon anterior cruciate ligament reconstruction surgery. Clinical Biomechanics 26(2):175-180

    Google Scholar 

  19. Ryan A S, Dobrovolny C L, Smith G V, Silver K H, Macko R F (2002) Hemiparetic muscle atrophy and increased intramuscular fat in stroke patients. Archives of physical medicine and rehabilitation 83(12):1703-1707

    Google Scholar 

  20. http://www.microsoft.com/en-us/kinectforwindows/

  21. Poppe R (2007) Vision-based human motion analysis: An overview. Computer vision and image understanding 108(1):4-18

    Google Scholar 

  22. Goffredo M, Schmid M, Conforto S, Carli M, Neri A, Alessio T D (2009) Markerless human motion analysis in Gauss–Laguerre transform domain: An application to sit-to-stand in young and elderly people. Information Technology in Biomedicine IEEE Transactions on 13(2):207-216

    Google Scholar 

  23. Corazza S, Muendermann L, Chaudhari A M, Demattio T, Cobelli C, Andriacchi T P (2006) A markerless motion capture system to study musculoskeletal biomechanics: visual hull and simulated annealing approach. Annals of biomedical engineering 34(6):1019-1029

    Google Scholar 

  24. Chang Y J, Chen S F, Huang J D (2011) A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in developmental disabilities 32(6):2566-2570

    Google Scholar 

  25. Goffredo M, Schmid M, Conforto S, D’Alessio T (2013) 3D Reaching in Visual Augmented Reality Using Kinect™: The Perception of Virtual Target. In Converging Clinical and Engineering Research on Neurorehabilitation 711-715

    Google Scholar 

  26. Clark R A, Pua Y H, Fortin K, Ritchie C, Webster K E, Denehy L, Bryant A L (2012) Validity of the Microsoft Kinect for assessment of postural control. Gait posture 36(3):372-377

    Google Scholar 

  27. Dutta T (2012) Evaluation of the Kinect™ sensor for 3-D kinematic measurement in the workplace. Applied ergonomics 43(4):645-649

    Google Scholar 

  28. Castelli A, Paolini G, Cereatti A, Della Croce U (2015) A 2D Markerless Gait Analysis Methodology: Validation on Healthy Subjects. Computational and Mathematical Methods in Medicine

    Google Scholar 

  29. Obdrzalek S, 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 Engineering in medicine and biology society (EMBC), 2012 annual international conference of the IEEE 1188-1193

    Google Scholar 

  30. Greff K, Brandão A, Krauß S, Stricker D, Clua E (2012) A Comparison between Background Subtraction Algorithms using a Consumer Depth Camera. In VISAPP 1:431-436

    Google Scholar 

  31. Conforto S, Schmid M, Neri A, D’Alessio T (2006) A neural approach to extract foreground from human movement images. Computer Methods and Programs in Biomedicine 82(1):73-80

    Google Scholar 

  32. Xia L, Chen C C, Aggarwal J K (2011) Human detection using depth information by Kinect. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on 15-22

    Google Scholar 

  33. Khoshelham K, Elberink S O (2012) Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12(2):1437-1454

    Google Scholar 

  34. Dryden I L, Mardia KV (1998) Statistical shape analysis. Chichester, West Sussex, UK: John Wiley Sons

    Google Scholar 

  35. Bookstein F L (1991) Morphometric tools for landmark data. Cambridge University Press

    Google Scholar 

  36. Le H, Kume A 2000 Detection of shape changes in biological features. Journal of Microscopy 200(2):140-147

    Google Scholar 

  37. Varano V, Gabriele S, Teresi L, Dryden I, Puddu P, Torromeo C, Piras P (2015) Comparing shape trajectories of biological soft tissues in the size-and-shape space. In biomat 2014 International Symposium on Mathematical and Computational Biology 351-365

    Google Scholar 

  38. Piras P, Evangelista A, Gabriele S, Nardinocchi P, Teresi L, Torromeo C et al. (2014) 4D-Analysis of Left Ventricular Heart Cycle Using Procrustes Motion Analysis. PLoS ONE 9(1):86896

    Google Scholar 

  39. Madeo A, Piras P, Re F, Gabriele S, Nardinocchi P, Teresi L, et al. (2015) A New 4D Trajectory-Based Approach Unveils Abnormal LV Revolution Dynamics in Hypertrophic Cardiomyopathy. PLoS ONE 10(4):0122376

    Google Scholar 

  40. Goss C M (1960) Gray’s Anatomy of the Human Body. Academic Medicine, 35(1):90

    Google Scholar 

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Correspondence to Michela Goffredo .

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Goffredo, M., Piras, P., Varano, V., Gabriele, S., D’Anna, C., Conforto, S. (2016). Shape Analysis of Bicipital Contraction by Means of RGB-D Sensor, Parallel Transport and Trajectory Analysis. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_122

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  • DOI: https://doi.org/10.1007/978-3-319-32703-7_122

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