  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.
