As in many other fields of application, the availability of miniaturized sensors for measuring a wide variety of parameters, powerful computer technologies, and a wide range of artificial intelligence methods opens more and more support possibilities for training and exercise in sports. We present current developments and prospects for the use of respective systems and illustrate this with examples from our research. These include technologies and systems based on the automated recognition of movements, which can, for example, make recommendations in the event of incorrectly executed forms of movement, initiate immediate reactions in critical situations, or allow interaction in virtual environments. In addition, systems for providing feedback and recommending individual exercises based on personal performance levels, as well as data-based methods for training control or predicting performance gains through the type and amount of training that do not interfere with physical activity, are highlighted. The expected potential of such innovative approaches, limitations, and risks are critically reflected.

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Perspectives of Artificial Intelligence in Training and Exercise

  • Arnold Baca

摘要

As in many other fields of application, the availability of miniaturized sensors for measuring a wide variety of parameters, powerful computer technologies, and a wide range of artificial intelligence methods opens more and more support possibilities for training and exercise in sports. We present current developments and prospects for the use of respective systems and illustrate this with examples from our research. These include technologies and systems based on the automated recognition of movements, which can, for example, make recommendations in the event of incorrectly executed forms of movement, initiate immediate reactions in critical situations, or allow interaction in virtual environments. In addition, systems for providing feedback and recommending individual exercises based on personal performance levels, as well as data-based methods for training control or predicting performance gains through the type and amount of training that do not interfere with physical activity, are highlighted. The expected potential of such innovative approaches, limitations, and risks are critically reflected.