Dexterous use of the hand represents a sophisticated sensorimotor function. In behaviors such as playing the piano, it can involve strong temporal and spatial constraints. The purpose of this study was to determine fundamental patterns of covariation of motion across joints and digits of the human hand. Joint motion was recorded while 5 expert pianists played 30 excerpts from musical pieces, which featured ∼50 different tone sequences and fingering. Principal component analysis and cluster analysis using an expectation-maximization algorithm revealed that joint velocities could be categorized into several patterns, which help to simplify the description of the movements of the multiple degrees of freedom of the hand. For the thumb keystroke, two distinct patterns of joint movement covariation emerged and they depended on the spatiotemporal patterns of the task. For example, the thumb-under maneuver was clearly separated into two clusters based on the direction of hand translation along the keyboard. While the pattern of the thumb joint velocities differed between these clusters, the motions at the metacarpo-phalangeal and proximal-phalangeal joints of the four fingers were more consistent. For a keystroke executed with one of the fingers, there were three distinct patterns of joint rotations, across which motion at the striking finger was fairly consistent, but motion of the other fingers was more variable. Furthermore, the amount of movement spillover of the striking finger to the adjacent fingers was small irrespective of the finger used for the keystroke. These findings describe an unparalleled amount of independent motion of the fingers.
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http://dx.doi.org/10.1152/jn.00378.2011 | DOI Listing |
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School of Kinesiology, University of Michigan, Ann Arbor, Michigan.
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Graduate School of Health Sciences, Morinomiya University of Medical Sciences, Osaka, Japan.
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Department of Sport and Health Sciences, Technical University of Munich, Munich, BY, GERMANY.
In weightlifting, quantitative kinematic analysis is essential for evaluating snatch performance. While marker-based (MB) approaches are commonly used, they are impractical for training or competitions. Markerless video-based (VB) systems utilizing deep learning-based pose estimation algorithms could address this issue.
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January 2025
Faculty of Sport Science, Ningbo University, Ningbo, 315211, China.
The long jump is an athletic event that demands speed, power, force application, and balance, with each phase being critical to overall performance. However, previous research has neglected the limiting effect of the wedge pedals on ankle dorsiflexion range of motion. This cross-sectional study investigated biomechanical changes in the lower extremities during long jumps under varying degrees of ankle dorsiflexion.
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