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Physical activity recognition is an important research area in pervasive computing because of its importance for in e-healthcare, security and human-machine interaction. Among various approaches, passive RF sensing on the basis of well-tried radar principle has potential to provides unique non-invasive human activity detection and recognition solution, and draws more attention. However, this technology is far from mature. This paper presents a novel HMM-log-likelihood matrix based feature characterizing of the Doppler shifts to break the fixed sliding window limitation in traditional feature extraction approaches. We prove the effectiveness of proposed feature extraction method in K-means&K-medoids clustering algorithms with experimental Doppler data gathered collected from a passive radar system. The time adaptive log-likelihood matrix-based approach outperforms the traditional SVD, PCA and physical feature based approaches, and reaches 80% in activity recognizing rate.

Purpose: We present an original method for simulating realistic fetal neurosonography images specifically generating third-trimester pregnancy ultrasound images from second-trimester images. Our method was developed using unpaired data as pairwise data were not available. We also report original insights on the general appearance differences between second-and third-trimester fetal head transventricular (TV) plane images.Approach: We design a cycle-consistent adversarial network (Cycle-GAN) to simulate visually realistic third-trimester images from unpaired second-and third-trimester ultrasound images. Simulation realism is evaluated qualitatively by experienced sonographers who blindly graded real and simulated images. A quantitative evaluation is also performed whereby a validated deeplearning-based image recognition algorithm (ScanNav ® ) acts as the expert reference to allow hundreds of real and simulated images to be automatically analyzed and compared efficiently.Results: Qualitative evaluation shows that the human expert cannot tell the difference between real and simulated third-trimester scan images. 84.2% of the simulated third-trimester images could not be distinguished from the real third-trimester images. As a quantitative baseline, on 3000 images, the visibility drop of the choroid, CSP, and mid-line falx between real second-and real third-trimester scans was computed by ScanNav ® and found to be 72.5%, 61.5%, and 67%, respectively. The visibility drop of the same structures between real second-trimester and simulated third-trimester was found to be 77.5%, 57.7%, and 56.2%, respectively. Therefore, the real and simulated third-trimester images were consider to be visually similar to each other. Our evaluation also shows that the third-trimester simulation of a conventional GAN is much easier to distinguish, and the visibility drop of the structures is smaller than our proposed method. Conclusions:The results confirm that it is possible to simulate realistic third-trimester images from second-trimester images using a modified Cycle-GAN, which may be useful for deep learning researchers with a restricted availability of third-trimester scans but with access to ample second trimester images. We also show convincing simulation improvements, both qualitatively and quantitatively, using the Cycle-GAN method compared with a conventional GAN. Finally, the use of a machine learning-based reference (in the case ScanNav ® ) for large-scale quantitative image analysis evaluation is also a first to our knowledge.

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