The motion of the finger is made up of a combination of forearm part (extrinsic) muscles and hand part (intrinsic) muscles.  We have created a wearable fingerless glove controller to sense sEMG (surface Electromyography) from intrinsic muscles using dry electrodes.  Recognition of air-tapping gesture with a sensor attached to wearable fingerless glove controller is a challenging problem.  In this study, we focused on motion recognition of air-tapping and performed motion recognition using CNN and evaluated its ac-curacy.  As a result, the accuracy in intra-subject identification was 85.05%.  Also, experiments are currently being conducted in anticipation of character input in VR space.  Character input experiment in VR space was carried out using sEMG wearable fingerless glove controller, as a primitive experiment of the use of sEMG glove in VR space.  Based on the results, we discussed the efficiency of character input using sEMG glove in VR space.