The state of neck motion reflects cervical health. To detect the motion state of the human neck is of important significance to healthcare intelligence. A practical neck motion detector should be wearable, flexible, power efficient, and low cost. Here, we report such a neck motion detector comprising a self-powered triboelectric sensor group and a deep learning block. Four flexible and stretchable silicon rubber based triboelectric sensors are integrated on a neck collar. With different neck motions, these four sensors lead-out voltage signals with different amplitudes and/or directions. Thus, the combination of these four signals can represent one motion state. Significantly, a carbon-doped silicon rubber layer is attached between the neck collar and the sensors to shield the external electric field (i.e., electrical changes at the skin surface) for a far more robust identification. Furthermore, a deep learning model based on the convolutional neural network is designed to recognize 11 classes of neck motion including eight directions of bending, two directions of twisting, and one resting state with an average recognition accuracy of 92.63%. This developed neck motion detector has promising applications in neck monitoring, rehabilitation, and control.
The application of deep learning (DL) in various brain computer interface (BCI) systems has achieved great success, but the results on the attention classification task are still not satisfactory. In this paper, an end-to-end mixed neural network model was proposed to classify the attention and nonattention mental states from multi-channel electroencephalography (EEG) data. During the experiment, a cross-subject strategy was performed on the attention detection task. Evaluated on a different electrodes combination of a publicly available dataset, the proposed model outperforms these baseline methods while maintaining relatively low computational complexity. The improved performance is meaningful for the attentive mental state classification task and is useful for the process of attention enhancement.
Video object segmentation aims at separating foreground object from background, and it is far from well solved for different challenges such as deformation, occlusion and motion blurs. This paper proposes a robust video object segmentation method by propagating patch seams and matching superpixels. First, we predict the initial object contour based on pixel-level target labels calculated by patch seam propagation and rough sets. By a patch seam, we map a current patch to its most similar patch from last frame and obtain its labels based on the labels of mapped patch. Second, we utilize superpixels as middle level cues to optimize predicted object contour. The bidirectional distance based on three brightness channels is provided to match superpixels between adjacent frames. Using the boundaries of matched results and initialized object contour, many candidates of object contours are constructed. Third, we define an energy function based on multi-features to measure contour candidates, and the contour with minimum energy is the final segmented result of current frame. Finally, by propagating patch seams and matching superpixels, we compute video object segmentation results frame by frame. Fourteen videos of SegTrack-v2 data are used to evaluate our method. The quantitative and qualitative evaluations show that our method performs better than most present methods especially in dealing with occlusion, deformation and motion blurs.
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