An Attention Training System Combining Immersive Vision and Discrete Force Control Tasks

Abstract

The invention discloses an immersive attention training system combining vision and discrete force control tasks. The system, by frequently conducting attention and finger force focusing activity training and through an adaptive algorithm, can automatically regulate the difficulty of training tasks so as to adapt to increasing control capacity of a user or individual difference of different persons; and meanwhile, the initiative and attention input of the user in the training tasks can be constantly kept at a maximum level, so that the purpose of improving the attention and finger force control of target people is achieved.

Description

technical field [0001] The invention relates to the technical field of precise force control training of attention and finger force pressing, in particular to an attention training system combining immersive vision and discrete force control tasks. Background technique [0002] Special occupations with very high attention requirements, such as pilots, racing drivers, airport tower air traffic control and other professionals need to have a high degree of attention control ability to ensure the force control and time accuracy of key tasks. It goes without saying that improving the concentration of attention will improve the work efficiency of employees, so attention training is also of great benefit to the training of ordinary adults or the human resources departments of enterprises and institutions. [0003] The applicable objects of the existing attention control training devices are usually aimed at specific groups of people, such as utility model patents CN205127306U and C...

AI Technical Summary

Problems solved by technology

If the task difficulty is too low, the success rate of the training game is often close to 100%, giving users a lower and lower sense of accomplishment; if the task difficulty is too high, the success rate of the training game is always less than 50%, which will cause users to suffer huge losses Frustration, both of which will greatly reduce the effect and attractiveness of the later training

At present, how to monitor the real-time state of the user's ability to control the specified game, and adjust the difficulty of the training task in real time accordingly, so that the user can always obtain the preset success rate in the training game is still an unresolved problem

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