The inertial measurement unit (IMU) is a popular sensor device, which is mainly employed to acquire body or hand gesture action information for performing specific recognition tasks. We present a dual-channel artificial neural network (ANN) recognition decision hybridization scheme incorporated with deep leaning of IMU-based spectrogram images for cognition of several common hand gesture intention categorization actions focused on the 6-axis IMU sensing data (containing 3-axis accelerometer and

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... 3-axis gyroscope information) and the 6-axis IMU derived spectrogram images. In this hand gesture intention cognition approach, both symmetric and asymmetric ANN structures are considered for intention action classifications. The proposed dual-channel ANN decision fusion framework contains one ANN recognition channel with inputs of "6-axis IMU raw data" and the other ANN recognition channel with inputs of "IMU spectrogram image derived-critical deep learning features". Recognition decisions estimated from either of these two ANN recognition channels form the fusion framework. Three fusion schemes on dual-channel ANN recognition decisions are presented in this study, channel output layer accumulation, same channel candidate output and same-or-dual channel candidate output. In this study, the well-known deep learning neural network, visual geometry group-convolution neural network (VGG-CNN), is employed to carry out deep learning computations on IMU-based spectrogram images, from which, the critical deep learning feature of each spectrogram image can then be extracted and used as an input for the dual-channel ANN. For recognition performance comparisons, hand gesture intention recognition by the traditional VGG-CNN deep neural network approach (i.e. recognition of IMU spectrogram images using typical deep learning of the CNN model) is also performed. Experiments on classifications of six hand gesture intention actions show that the presented dual-channel ANN decision fusion incorporated with deep learning of IMU spectrum images has competitive performances, reaching better recognition accuracy than traditional CNN deep learning.