Ego-perspective enhanced fitness training experience of AR Try to Move game
This work addresses the need for more engaging and effective remote rehabilitation training for users undergoing upper limb sports rehabilitation, though it appears incremental by applying existing AR and CNN methods to this specific domain.
The paper tackled the problem of boring and inefficient upper limb rehabilitation training by developing an AR game with a CNN for gesture recognition, resulting in a system that incentivizes users to enhance their upper limb muscle system through remote training with greater effectiveness and convenience.
AR, a recent emerging technology, has been widely used in entertainment to provide users with immersive, interactive, and, sometimes, engaging experiences. The process of rehabilitation treatment and motor training process is often boring, and it is well known that users' exercise efficiency is often not as efficient as in a rehabilitation institution. Thus far, there is no effective upper limb sports rehabilitation training game based on the ego-perspective. Hence, with the objective of enhancing the enjoyment experience in rehabilitation and more effective remote rehabilitation training, this work aims to provide an AR Try to Move game and a convolutional neural network (CNN) for identifying and classifying user gestures from a self-collected AR multiple interactive gestures dataset. Utilizing an AR game scoring system, users are incentivized to enhance their upper limb muscle system through remote training with greater effectiveness and convenience.