ABSTRACT
Cognitive functions, motoric expression, and changes in physiology are often studied separately, with little attention to the relationships, or correlations, among these entities. In this study, we implement an integrated approach by combining motion capture (action) and EMG (physiological) parameters as synchronized data streams resulting from the action and associated physiological data. Our experiments were designed to measure the preparatory movement capabilities of the upper extremities. In particular, measurement of changes in preparatory activity during the aging process are of interest to us, as the attempt is to develop means to compensate for loss of adaptive capabilities that aging entails. To achieve this goal, it is necessary to quantify preparation phases (timing and intensity). We measured motion capture and EMG parameters when subjects raised their arms without constraint (condition one) and raised their arms while holding a ball (second condition). Furthermore, on comparing aging and young participants, we confirmed that with aging the temporal relationships between actual movement and the preceding EMG signal change.
- D. Falla, A. Rainoldi, R. Merletti, and G. Jull. Spatio-temporal evaluation of neck muscle activation during postural perturbations in healthy subjects. Journal of Electromyography and Kinesiology, 14(4):463--474, 2004.Google ScholarCross Ref
- E. Finch. Physical rehabilitation outcome measures: a guide to enhanced clinical decision making. Lippincott Williams and Wilkins, 2002.Google Scholar
- R. Gottsdanker. Age and simple reaction time. Journal of Gerontology, 37:342--348, 1982.Google ScholarCross Ref
- R. Jagacinski, N. Greenberg, M. Liao, and J. Wang. Manual performance of a repeated pattern by older and younger adults with supplementary auditory cues. Psychology and Aging, 8:429--439, 1993.Google ScholarCross Ref
- F. Lacquaniti and C. Maioli. The role of preparation in tuning anticipatory and reflex responses during catching. Journal of Neuroscience, 9(1):134--148, 1989.Google ScholarCross Ref
- N. Lupinacci, R. Rikli, C. Jones, and D. Rose. Age and physical activity effects on reaction time and digit symbol substitution performance in cognitively active adults. Research Quarterly for Exercise and Sport, 64:144--150, 1993.Google ScholarCross Ref
- P. McCrea, J. Eng, and A. Hodgson. Biomechanics of reaching: clinical implications for individuals with acquired brain injury. Journal of Disability and Rehabilitation, 24(10):534--541, 2002.Google ScholarCross Ref
- I. Melzer, N. Benjuya, and J. Kaplanski. Age-related changes of postural control: effect of cognitive tasks. Gerontology, 47(4):189--194, 2001.Google ScholarCross Ref
- M. A. Murphy, K. S. Sunnerhagen, B. Johnels, and C. Willén. Biomechanics of reaching: clinical implications for individuals with acquired brain injury. Journal of NeuroEngineering and Rehabilitation, 3(18):1--11, 2006.Google Scholar
- M. Nadin. Mind - Anticipation and Chaos. Belser Presse, 1991.Google Scholar
- M. Pijnappels, M. Bobbert, and J. van Dieën. Emg modulation in anticipation of a possible trip during walking in young and older adults. Journal of Electromyography and Kinesiology, 16(2): 137--143, 2005.Google ScholarCross Ref
- G. Pradhan, N. Engineer, and M. N. and Balakrishnan Prabhakaran. An integrated mobile wireless system for capturing physiological data streams during a cognitive-motor task: Applications for aging. In IEEE Dallas Engineering in Medicine and Biology Workshop, pages 67--70, November 2007.Google ScholarCross Ref
- G. Rau, C. Disselhorst-Klug, and R. Schmidt. Movement biomechanics goes upwards: from the leg to the arm. Journal of Biomechanics, 33(10):1207--1216, 2000.Google ScholarCross Ref
- D. Wade. Measurement in neurological rehabilitation. Oxford University Press, 1992.Google Scholar
- J. Yan, J. Thomas, and G. Stelmach. Aging and rapid aiming arm movement control. Experimental Aging Research, 24(2):155--168, 1998.Google ScholarCross Ref
Index Terms
Analyzing motoric and physiological data in describing upper extremity movement in the aged
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