Real-Time Spatiotemporal Databases to Support Human Motor Skills

Author(s):  
Giuseppe Averta ◽  
Visar Arapi ◽  
Antonio Bicchi ◽  
Cosimo della Santina ◽  
Matteo Bianchi

Retos ◽  
2021 ◽  
Vol 42 ◽  
pp. 924-938
Author(s):  
Eliseo Andreu Cabrera ◽  
Francisco Javier Romero-Naranjo

  El propósito de este artículo es analizar la terminología relacionada con la motricidad humana, al objeto de proponer el neologismo de neuromotricidad como concepto del siglo XXI, que se diferencie conceptualmente de otros términos similares, como motricidad o psicomotricidad. Los avances en el estudio del cerebro y su relación con el movimiento, nos empuja a la creación de un área especial dentro de la Ciencia de la motricidad humana, con planteamientos metodológicas innovadores. El método BAPNE (Romero, 2004) se postula como una posibilidad para optimizar el rendimiento del cerebro en el ámbito de la motricidad humana. Así mismo, aportamos una pirámide explicativa que muestra gráficamente la jerarquía terminológica dentro del ámbito de la motricidad, tanto a nivel teórico como práctico. Abstract: The purpose of this article is to analyse the terminology related to human motor skills in order to propose the neologism of neuromotor skills (Neuromotricity) as a concept for the 21st century, which is conceptually different from other similar terms such as motor skills or psychomotor skills. Advances in the study of the brain and its relationship with movement have led to the creation of a special area within the science of human motor skills, with innovative methodological approaches. The BAPNE method (Romero, 2004) is postulated as a possibility to optimise the performance of the brain in the field of human motor skills. We also provide an explanatory pyramid that graphically shows the terminological hierarchy within the field of motor skills, both on a theoretical and practical level.


Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 40
Author(s):  
Hiroki Yokota ◽  
Munekazu Naito ◽  
Naoki Mizuno ◽  
Shigemichi Ohshima

In this research, we propose a visual-feedback system and evaluate it based on motion-sensing and computational technologies. This system will help amateur athletes imitate the motor skills of professionals. Using a self-organizing map (SOM) to visualize high-dimensional time-series motion data, we recorded the cyclic motion information, including the muscle activities, of a male subject as he pedaled a bicycle ergometer. To clarify the difference between the subject’s motor skill and the target motor skill in a cyclic movement, we used the modified SOM algorithm; a visual-feedback system was developed, which displayed the target motion as a circular trajectory on a two-dimensional motor skills map. The subject trained by observing only the displayed static target trajectory; the subject’s real-time trajectory was constructed from the subject’s real-time motion. We validated our proposed framework for the visual-feedback system by evaluating the motion performance of a subject using feedback training.


1993 ◽  
Vol 9 (6) ◽  
pp. 709-722 ◽  
Author(s):  
G.A. Bekey ◽  
Huan Liu ◽  
R. Tomovic ◽  
W.J. Karplus

2010 ◽  
Vol 121 ◽  
pp. S18
Author(s):  
T. Yoshimine ◽  
T. Yanagisawa ◽  
R. Fukuma ◽  
T. Goto ◽  
Y. Kamitani ◽  
...  
Keyword(s):  

2011 ◽  
Vol 20 (1) ◽  
pp. 62-77 ◽  
Author(s):  
Daniel L. Eaves ◽  
Gavin Breslin ◽  
Paul van Schaik

Does virtual reality (VR) represent a useful platform for teaching real-world motor skills? In domains such as sport and dance, this question has not yet been fully explored. The aim of this study was to determine the effects of two variations of real-time VR feedback on the learning of a complex dance movement. Novice participants (n = 30) attempted to learn the action by both observing a video of an expert's movement demonstration and physically practicing under one of three conditions. These conditions were: full feedback (FULL-FB), which presented learners with real-time VR feedback on the difference between 12 of their joint center locations and the expert's movement during learning; reduced feedback (REDUCED-FB), which provided feedback on only four distal joint center locations (end-effectors); and no feedback (NO-FB), which presented no real-time VR feedback during learning. Participants' kinematic data were gathered before, immediately after, and 24 hr after a motor learning session. Movement error was calculated as the difference in the range of movement at specific joints between each learner's movement and the expert's demonstrated movement. Principal component analysis was also used to examine dimensional change across time. The results showed that the REDUCED-FB condition provided an advantage in motor learning over the other conditions: it achieved a significantly greater reduction in error across five separate error measures. These findings indicate that VR can be used to provide a useful platform for teaching real-world motor skills, and that this may be achieved by its ability to direct the learner's attention to the key anatomical features of a to-be-learned action.


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