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Human pose estimation localizes body keypoints to accurately recognizing the postures of individuals given an image. This step is a crucial prerequisite to multiple tasks of computer vision which include human action recognition, human tracking, human-computer interaction, gaming, sign languages, and video surveillance. Therefore, we present this survey article to fill the knowledge gap and shed light on the researches of 2D human pose estimation. A brief introduction is followed by classifying it as a single or multi-person pose estimation based on the number of people needed to be tracked. Then gradually the approaches used in human pose estimation are described before listing some applications and also flaws facing in pose estimation. Following that, a center of attention is given on briefly discussing researches with a significant effect on human pose estimation and examine the novelty, motivation, architecture, the procedures (working principles) of each model together with its practical application and drawbacks, datasets implemented, as well as the evaluation metrics used to evaluate the model. This review is presented as a baseline for newcomers and guides researchers to discover new models by observing the procedure and architecture flaws of existing researches.

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Extracellular chronic recordings have been used as important evidence in neuroscientific studies to unveil the fundamental neural network mechanisms in the brain. Spike detection is the first step in the analysis of recorded neural waveforms to decipher useful information and provide useful signals for brain-machine interface applications. The process of spike detection is to extract action potentials from the recordings, which are often compounded with noise from different sources. This study proposes a new detection algorithm that leverages a technique from wavelet-based image edge detection. It utilizes the correlation between wavelet coefficients at different sampling scales to create a robust spike detector. The algorithm has one tuning parameter, which potentially reduces the subjectivity of detection results. Both artificial benchmark data sets and real neural recordings are used to evaluate the detection performance of the proposed algorithm. Compared with other detection algorithms, the proposed method has a comparable or better detection performance. In this letter, we also demonstrate its potential for real-time implementation.

Inflammatory damage plays an important role in cerebral ischemic pathogenesis and may represent a target for treatment. Toll-like receptor-4 (TLR4), toll-like receptor-2 (TLR2), myeloid differentiation factor 88 (MyD88), and nuclear factor kappa-B (NF-κB) have been linked to inflammatory reactions. Our previous studies have proved that oxymatrine (OMT) protected ischemic brain injury and this effect may be through the decreasing of NF-κB expression. However, little is known regarding the mechanism of OMT in the acute phase of ischemic stroke. We therefore investigated the OMT's potential neuroprotective role and the underlying mechanisms. Male, Sprague-Dawley rats were randomly divided into sham, saline and OMT treatment groups. We used a middle cerebral artery occlusion (MCAO) model and administered OMT intraperitoneally immediately after cerebral ischemia and once daily on the following days. At time points after MCAO, brain water content and infarct size were measured. Immunohistochemistry and RT-PCR were used to analyse the expression of TLR4, TLR2, MyD88, and NF-κB at gene and protein level in ischemic brain tissue. The result indicated that OMT protected the brain from damage caused by MCAO; this effect may be through downregulation of the TLR4, TLR2, MyD88, and NF-κB.