Abstract
This study aimed to quantitatively investigate upper extremity motor performance and disclose the abnormality of motor control induced by stroke. Ten patients and ten healthy subjects were instructed to perform target-reaching tasks at nine difficulty levels, and coordinates of the shoulder, elbow and tip of the index finger were recorded. Age-matched control performed significantly better than patients, as indicated by lower movement time (MT) and normalized jerk score (NJS) and higher peak velocity (V peak), percentage time to peak velocity (PTPV), fuzzy approximate entropy (fApEn) and relative joint angles correlation (RJAC); also, significant effects of difficulty on all parameters except RJAC and fApEn, were observed in two groups. There were significant correlations between PTPV and Fugl-Meyer assessment for upper extremity (FMA-UE) and between RJAC and FMA-UE at certain difficulty levels. The stroke-related differences could be explained by the increase in intrinsic neuromotor noise, and the difficulty-related differences may be related to extrinsic neuromotor noise. The increase in either noises could result in a degradation in motor control. The significant linear relationships between some kinematic parameters and the clinical score suggested that the kinematic parameters could be applied as quantitative outcome measures in the clinic in the future.
Similar content being viewed by others
References
Ao, D., R. Sun, K. Y. Tong, and R. Song. Characterization of stroke-and aging-related changes in the complexity of EMG signals during tracking tasks. Ann. Biomed. Eng. 43:990–1002, 2015.
Berthier, N. E., R. K. Clifton, V. Gullapalli, D. D. McCall, and D. J. Robin. Visual information and object size in the control of reaching. J. Mot. Behav 28:187–197, 1996.
Buck, D., A. Jacoby, A. Massey, and G. Ford. Evaluation of measures used to assess quality of life after stroke. Stroke 31:2004–2010, 2000.
Chang, J. J., T. I. Wu, W. L. Wu, and F. C. Su. Kinematical measure for spastic reaching in children with cerebral palsy. Clin. Biomech. 20:381–388, 2005.
Cozens, J. A., and B. B. Bhakta. Measuring movement irregularity in the upper motor neurone syndrome using normalised average rectified jerk. J. Electromyogr. Kinesiol. 13:73–81, 2003.
Desmurget, M., and S. Grafton. Forward modeling allows feedback control for fast reaching movements. Trends Cogn. Sci. 4:423–431, 2000.
Dipietro, L., H. I. Krebs, S. E. Fasoli, B. T. Volpe, J. Stein, C. Bever, and N. Hogan. Changing motor synergies in chronic stroke. J. Neurophysiol. 98:757–768, 2007.
Fitts, P. M. The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47:381, 1954.
Geddes, J., J. Fear, A. Tennant, A. Pickering, M. Hillman, and M. A. Chamberlain. Prevalence of self reported stroke in a population in northern England. J. Epidemiol. Community Health 50:140–143, 1996.
Hall, K. M., N. Mann, W. M. High, Jr, J. Wright, J. S. Kreutzer, and D. Wood. Functional measures after traumatic brain injury: Ceiling effects of FIM, FIM + FAM, DRS, and CIQ. J. Head Trauma Rehabil. 11:27–39, 1996.
Hammond, M., S. Fitts, G. Kraft, P. Nutter, M. Trotter, and L. Robinson. Co-contraction in the hemiparetic forearm: quantitative EMG evaluation. Arch. Phys. Med. Rehabil. 69:348–351, 1988.
Hermsdörfer, J., E. Hagl, and D. A. Nowak. Deficits of anticipatory grip force control after damage to peripheral and central sensorimotor systems. Hum. Mov. Sci. 23:643–662, 2004.
Hong, S. L., and K. M. Newell. Visual information gain and the regulation of constant force levels. Exp. Brain Res. 189:61–69, 2008.
Kudoh, N., M. Hattori, N. Numata, and K. Maruyama. An analysis of spatiotemporal variability during prehension movements: effects of object size and distance. Exp. Brain Res. 117:457–464, 1997.
Kwakkel G., B. J. Kollen and H. I. Krebs. Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabil. Neural Repair 2007.
Maitra, K. K., and M. D. Junkins. Upper extremity movement pattern of a common drinking task in well elderly women: a pilot study. Occup. Ther. Int. 11:67–81, 2004.
McCrea, P. H., and J. J. Eng. Consequences of increased neuromotor noise for reaching movements in persons with stroke. Exp. Brain Res. 162:70–77, 2005.
Miall, R. C., D. Weir, and J. Stein. Intermittency in human manual tracking tasks. J. Mot. Behav. 25:53–63, 1993.
Murphy, M. A., K. S. Sunnerhagen, B. Johnels, and C. Willén. Three-dimensional kinematic motion analysis of a daily activity drinking from a glass: a pilot study. J. Neuroeng. Rehabil. 3:1, 2006.
Murphy, M. A., C. Willén, and K. S. Sunnerhagen. Kinematic variables quantifying upper-extremity performance after stroke during reaching and drinking from a glass. Neurorehabil. Neural Repair 25:71–80, 2011.
Nagasaki, H. Asymmetric velocity and acceleration profiles of human arm movements. Exp. Brain Res. 74:319–326, 1989.
Naik, S. K., C. Patten, N. Lodha, S. A. Coombes, and J. H. Cauraugh. Force control deficits in chronic stroke: grip formation and release phases. Exp. Brain Res. 211:1–15, 2011.
Nordin, N., S. Q. Xie, and B. Wünsche. Assessment of movement quality in robot-assisted upper limb rehabilitation after stroke: a review. J. Neuroeng. Rehabil. 11:137, 2014.
Pohl, P. S., C. J. Winstein, and B. E. Fisher. The locus of age-related movement slowing: sensory processing in continuous goal-directed aiming. J. Gerontol. Ser. B Psychol. Sci. Soc. Sci. 51:P94–P102, 1996.
Przybyla, A., K. Y. Haaland, L. B. Bagesteiro, and R. L. Sainburg. Motor asymmetry reduction in older adults. Neurosci. Lett. 489:99–104, 2011.
Reft, J., and Z. Hasan. Trajectories of target reaching arm movements in individuals with spinal cord injury: effect of external trunk support. Spinal Cord 40:186–191, 2002.
Roby-Brami, A., A. Feydy, M. Combeaud, E. Biryukova, B. Bussel, and M. Levin. Motor compensation and recovery for reaching in stroke patients. Acta Neurol. Scand. 107:369–381, 2003.
Romilly, D. P., C. Anglin, R. G. Gosine, C. Hershler, and S. U. Raschke. A functional task analysis and motion simulation for the development of a powered upper-limb orthosis. IEEE Trans. Rehabil. Eng. 2:119–129, 1994.
Sivan, M., R. J. O’Connor, S. Makower, M. Levesley, and B. Bhakta. Systematic review of outcome measures used in the evaluation of robot-assisted upper limb exercise in stroke. J. Rehabil. Med. 43:181–189, 2011.
Skilbeck, C. E., D. T. Wade, R. L. Hewer, and V. A. Wood. Recovery after stroke. J. Neurol. Neurosurg. Psychiatry 46:5–8, 1983.
Sokunbi, M. O., G. G. Cameron, T. S. Ahearn, A. D. Murray, and R. T. Staff. Fuzzy approximate entropy analysis of resting state fmri signal complexity across the adult life span. Med. Eng. Phys. 37:1082–1090, 2015.
Sternad, D., M. O. Abe, X. Hu, and H. Müller. Neuromotor noise, error tolerance and velocity-dependent costs in skilled performance. PLoS Comput Biol 7:e1002159, 2011.
Todor, J. I., and J. Cisneros. Accommodation to increased accuracy demands by the right and left hands. J. Mot. Behav. 17:355–372, 1985.
van Beers, R. J., P. Haggard, and D. M. Wolpert. The role of execution noise in movement variability. J. Neurophysiol. 91:1050–1063, 2004.
van Meulen, F. B., J. Reenalda, J. H. Buurke, and P. H. Veltink. Assessment of daily-life reaching performance after stroke. Ann. Biomed. Eng. 43:478–486, 2015.
Welford, A. T. Signal, noise, performance, and age. Hum. Factors: J. Hum. Factors Ergon. Soc. 23:97–109, 1981.
Xie, H. B., J. Y. Guo, and Y. P. Zheng. Fuzzy approximate entropy analysis of chaotic and natural complex systems: detecting muscle fatigue using electromyography signals. Ann. Biomed. Eng. 38:1483–1496, 2010.
Acknowledgments
The authors would like to thank participants in the stroke rehabilitation teams of the First Affiliated Hospital of Sun Yat-sen University and the Sun Yat-sen Memorial Hospital, their enthusiastic participation in this study are gratefully appreciated. The project has been carried out with financial support from the National Natural Science Foundation of China (Grant No. 61273359 and 91520201), Guangdong Science and Technology Planning Project (Grant No. 2014B090901056 and 2015B020214003), and Guangzhou Key Lab of Body Data Science (Grant No. 201605030011).
Author information
Authors and Affiliations
Corresponding author
Additional information
Associate Editor Xiaoxiang Zheng oversaw the review of this article.
Rights and permissions
About this article
Cite this article
Yang, Q., Yang, Y., Luo, J. et al. Kinematic Outcome Measures using Target-Reaching Arm Movement in Stroke. Ann Biomed Eng 45, 2794–2803 (2017). https://doi.org/10.1007/s10439-017-1912-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10439-017-1912-7