  Human action detection is a hot topic, which is widely used in video
surveillance, human machine interface, healthcare monitoring, gaming, dancing
training and musical instrument teaching. As inertial sensors are low cost,
portable, and having no operating space, it is suitable to detect human action.
In real-world applications, actions that are of interest appear among actions
of non interest without pauses in between. Recognizing and detecting actions of
interests from continuous action streams is more challenging and useful for
real applications. Based on inertial sensor and C-MHAD smart TV gesture
recognition dataset, this paper utilized different inertial sensor feature
formats, then compared the performance with different deep neural network
structures according to these feature formats. Experiment results show the best
performance was achieved by image based inertial feature with convolution
neural network, which got 51.1% F1 score.
