Posts Tagged Motion measurement

[Abstract + References] Methods of Motion Assessment of Upper Limb for Rehabilitation Application – IEEE Conference Publication


The aim of this paper is to describe methods proposed for motion capture subsystem of smart orthosis for quantitative evaluation of movement activity of upper limbs during a rehabilitation process carried out at a clinic or at home. To quantify the description of motion we used methods of evaluation of the relationship between measured variables and nonlinear methods. To test the functionality of the methods, we compared the movement of the dominant and non-dominant limbs, assuming cyclical and acyclic movement, to obtain the expected values for a healthy population. In accordance with the goal, a group of cyclic and non-cyclic movements common to the home environment were proposed. The movements were divided according to the activities performed during sitting, standing and walking. It was: pen writing, typing on the keyboard / using the mouse, eating with a spoon and eating a croissant combing, lifting weights, reading a book, etc. Twenty healthy subjects participated in the study. Four gyro-accelerometers (Xsens Technologies B.V.) attached to the forearms and upper arms of both upper limbs were used to record the upper limb movements. The results show that the calculated values of dominant and non-dominant limb parameters differ significantly in most movements. The motion capture subsystem which uses the proposed methods can be used to valuate the physical activity for quantification of the evaluation of the rehabilitation process, and thus, it finds use in practice.
1. D. P Romilly, C Anglin, R. G Gosine, C Hershler, S. U. Raschke, “A Functional Task Analysis and Motion Simulation for the Development of a Powered Upper-Limb Orthosis”, IEEE Transactions on Rehabilitation Engineering, pp. 119-129, 1994.

2. R. Rupp, M. Rohm, M. Schneiders, A. Kreilinger, G. R Müller-Putz, “Functional rehabilitation of the paralyzed upper extremity after spinal cord injury by noninvasive hybrid neuroprostheses”, Proceedings of the IEEE, pp. 954-968, 2015.

3. R. C. Oldfield, “The assessment and analysis of handedness”, The Edinburgh inventory. Neuropsychologia, pp. 97-113, 1971.

4. P. Kutilek, O. Cakrt, J. Hejda, “Com-parative measurement of the head orientation using camera system and gyroscope system”, 13th Mediterranean conference on medical and biological engineering and computing Seville Spain IFMBE Proceedings Volume 41, pp. 1519-1522, 2013.

5. P. Kutilek, V. Socha, O. Cakrt, J. Schlenker, L. Bizovska, “Trajectory length of pitch vs. roll. Technique for assessment of postural stability”, Acta Gymnica, pp. 85-92, 2015.

6. J. H Allum, L. B. O. Nijhuis, M. G. Carpenter, “Differences in coding provided by proprioceptive and vestibular sensory signals may con-tribute to lateral instability in vestibular loss subjects”, Experimental brain research, vol. 184, no. 3, pp. 391-410, 2008.

7. Á. Gil-Agudo, L. A. Reyes-Guzman, Dimbwadyo-Terrer, I. Peñasco-Martín, B. Bernal-Sahún, A. P.López-Monteagudo, A. Ama-Espinosa, J. L Pons, “A novel motion tracking system for evaluation of functional rehabilitation of the upper limbs”, Neural regeneration research, vol. 8, no. 19, pp. 1773-1782, 2013.

8. D. Stirling, A. Hesami, C. Ritz, K. Kdistambha, F. Naghdy, “Symbolic Modelling of Dynamic Human Motions”, Biosensors. Pier Andrea Serra, 2013.

9. F. Lorussi, N. Carbonaro, D. D. Rossi, A. Tognetti, “A biarticular model for scapular-humeral rhythm reconstruction through data from wearable sensors”, J Neuroeng Rehabil, vol. 13, pp. 40, 2016.

10. D. Winter, “Stiffness Control of Balance in Quiet Standing”, Journal of Neurophysiology, pp. 1211-1221, 1998.

11. P. Kutilek, B. Farkasova, “Prediction of Lower Extremities’ Motion by Angle-angle Diagrams and Neural Networks”, Acta of Bioengineering and Biomechanics, pp. 57-65, 2011.

12. S. M. Bruijn, “Assessing Stability of Human Locomotion: a review of current measures” in Journal of the Royal Society Interface, 2013.

13. B. Coley, B. M. Jolles, A. Farron, A. Bourgeois, F. Nussbaumer, C. Pichonnaz, K. Aminian, “Outcome evaluation in shoulder surgery using 3D kinematics sensors”, Gait& Posture, vol. 25, pp. 523-532, 2007.

14. A. Wolf, J. B. Swift, H. L. Swinney, J. A. Vastano, “Determining Lyapunov exponents from a time series”, Physica 16D, pp. 285-317, 1985.

15. D. E. Lake, J. S. Richman, M. P. Griffin, J. R. Moorman, “Sample entropy analysis of neona-tal heart rate variability”, American Journal of Physiology – Regulatory Integrative and Comparative Physiology, vol. 283, no. 3, 2002.

16. M. O. Sokunbi, “Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets”, Front. Neuroinform, 2014.

17. B. Singh, M. Singh, V. K. Banga, “Sample Entropy based HRV: Effect of ECG Sampling Frequency”, Biomedical Science and Engineering, 2014.

18. Z. Jian-Jun, N. Xin-Bao, Y. Xiao-Dong, H. Feng-Zhen, H. Cheng-Yu, “Decrease in Hurst expo-nent of human gait with aging and neurodegenerative diseases”, Chin. Phys. Soc. and IOP Publishing Ltd Chinese Physics B, vol. 17, 2008.

19. A. Goshvarpour, A. Goshvarpour, “Nonlinear Analysis of Human Gait Signals”, International Journal of Information Engineering and Electronic Business(IJIEEB), vol. 4, pp. 15-21, 2012.

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[Abstract] Accurate upper body rehabilitation system using kinect


The growing importance of Kinect as a tool for clinical assessment and rehabilitation is due to its portability, low cost and markerless system for human motion capture. However, the accuracy of Kinect in measuring three-dimensional body joint center locations often fails to meet clinical standards of accuracy when compared to marker-based motion capture systems such as Vicon. The length of the body segment connecting any two joints, measured as the distance between three-dimensional Kinect skeleton joint coordinates, has been observed to vary with time. The orientation of the line connecting adjoining Kinect skeletal coordinates has also been seen to differ from the actual orientation of the physical body segment. Hence we have proposed an optimization method that utilizes Kinect Depth and RGB information to search for the joint center location that satisfies constraints on body segment length and as well as orientation. An experimental study have been carried out on ten healthy participants performing upper body range of motion exercises. The results report 72% reduction in body segment length variance and 2° improvement in Range of Motion (ROM) angle hence enabling to more accurate measurements for upper limb exercises.

I. Introduction

Body joint movement analysis is extremely essential for health monitoring and treatment of patients with neurological disorders and stroke. Chronic hemiparesis of the upper extremity following a stroke causes major hand movement limitations. There is possibility of permanent reduction in muscle coactivation and corresponding joint torque patterns due to stroke [1]. Several studies suggest that abnormal coupling of shoulder adductors with elbow extensors and shoulder abductors with elbow flexors often leads to some stereotypical movement characteristics exhibited by severe stroke patients [2]. Therefore continuous and effective rehabilitation therapy is absolutely essential to monitor and control such abnormalities. There is a substantial need for home-based rehabilitation post-clinical therapy.

Source: Accurate upper body rehabilitation system using kinect – IEEE Xplore Document

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