29th of January – 1st February 2014: Human Waves at 23rd ANT Burgundy Neuromeeting Sport Neurosciences
The Human Waves methodological approach The improvement of performance depends on force and cardiopulmonary parameters, as well as on neuromotor and ocular strategy. This information plays a crucial role in training strategy; however, the role of coaches is not to analyze complex results. Thus, Human Waves has developed a collaborative approach with the sporting team to foster athletic success. The objectives of Human Waves include: improving neuromuscular strategies, enhancing the mental control of muscular movement, decreasing the risk of injuries and aiding the movement of handicapped individuals. Our method is based on the integration of the PAMDA (Perception, Attention, Memorization, Decision, Action) paradigm with a technological platform that simultaneously measures cerebral, ocular and muscular activities, as well as the kinematic and dynamic aspects of movement. Five seconds of measurement recording can generate up to 6 million datapoints. Due to this large amount of information, Human Waves has developed “Easy Move,” a software program that first manages the input of data from numerous technological devices and then quickly analyzes this information. Easy Move generates a moving avatar which represents the athlete, alongside a graph of results that change simultaneously along with the avatar, showing the evolution of several parameters, including biomechanical elements (e.g. golf club speed, excessive foot rotation), activities of agonist and antagonist muscles, eye movements, etc. Results are displayed in an easy-to-understand manner, allowing one to observe changes in data as the athlete changes position. The analysis of brain activity measured during an athletic movement can be tricky due to the rejection of mechanical artefacts. We will compare two methods of analysis: the rejection of artefacts by using the independent component method of analysis, and the method of subtraction of the principal component, which will be analyzed with Easy Move. We will illustrate this approach through examples performed in soccer, hockey, track and golf.