Abstract
Technological advances enable the design of systems that interact more closely with humans in a multitude of previously unsuspected fields. Martial arts are not outside the application of these techniques. From the point of view of the modeling of human movement in relation to the learning of complex motor skills, martial arts are of interest because they are articulated around a system of movements that are predefined, or at least, bounded, and governed by the laws of Physics. Their execution must be learned after continuous practice over time. Literature suggests that artificial intelligence algorithms, such as those used for computer vision, can model the movements performed. Thus, they can be compared with a good execution as well as analyze their temporal evolution during learning. We are exploring the application of this approach to model psychomotor performance in Karate combats (called kumites), which are characterized by the explosiveness of their movements. In addition, modeling psychomotor performance in a kumite requires the modeling of the joint interaction of two participants, while most current research efforts in human movement computing focus on the modeling of movements performed individually. Thus, in this work, we explore how to apply a pose estimation algorithm to extract the features of some predefined movements of Ippon Kihon kumite (a one-step conventional assault) and compare classification metrics with four data mining algorithms, obtaining high values with them.
Highlights
The selection of kumite was not accidental. It was chosen because it challenges image processing in human movement computing, and there is little scientific literature on modeling the psychomotor performance in activities that involve the joint participation of several individuals, as in a karate combat
The movements performed by a karateka in front of an opponent may vary with respect to performing them alone through katas due to factors such as fear, concentration or adaptation to the opponent’s physique
The results obtained from the training of the classification algorithms with the features extracted from the recorded videos of different Kihon kumite postures and their application to non labelled images have been satisfactory
Summary
The scope of the sensors at present is large, with cheap and good quality sensors to be able to develop any system in any sector or specific field. In this way, HAR-type systems can gather data from diverse type of sensors such as: accelerometers (e.g., see [9,10]), gyroscopes, which are usually combined with an accelerometer (e.g., see [11,12]), GPS (e.g., see [13,14]), pulse-meters (e.g., see [15,16]), magnetometers (e.g., see [17,18]) and thermometers (e.g., see [19]). As analyzed in [20], the field is still emerging, and inertial-based sensors such as accelerometers and gyroscopes can be used to (i) recognize specific motion learning units and (ii) assess learning performance in a motion unit
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