An interactive image segmentation method in hand gesture recognition

In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy

Grasping force prediction based on sEMG signals

In order to realize the force control, when the prosthetic hand grasps the object, the forearm electromyography signal is collected by the multi-channel surface electromyography (sEMG) acquisition system. The grasping force information of the human hand is recorded by the six-dimensional force sensor. The root mean square (RMS) of the electromyography signal steady state is selected, which is an effective feature. The gene expression programming algorithm (GEP) and BP neural network are used to construct the prediction model and predict the grasping force. The force prediction accuracy of GEP algorithm and BP neural network algorithm are discussed under different grasping power levels and different grasping modes. The performance of the two algorithm models are evaluated by two measures of root mean square error (RMSE) and correlation coefficient (CC). The results show that the RMS eigenvalue extracted from the sEMG signal can better characterize the grasping force. The prediction model with GEP algorithm has smaller relative error and higher prediction effect

Sleep and energy intake in early childhood

Background And Objectives: Shorter sleep is associated with higher weight in children, but little is known about the mechanisms. The aim of this study was to test the hypothesis that shorter sleep was associated with higher energy intake in early childhood. Methods: Participants were 1303 families from the Gemini twin birth cohort. Sleep duration was measured using the Brief Infant Sleep Questionnaire when the children were 16 months old. Total energy intake (kcal per day) and grams per day of fat, carbohydrate and protein were derived from 3-day diet diaries completed by parents when children were 21 months old. Results: Shorter nighttime sleep was associated with higher total energy intake (P for linear trend=0.005). Children sleeping <10 h consumed around 50 kcal per day more than those sleeping 11–<12 h a night (the optimal sleep duration for children of this age). Differences in energy intake were maintained after adjustment for confounders. As a percentage of total energy intake, there were no significant differences in macronutrient intake by sleep duration. The association between sleep and weight was not significant at this age (P=0.13). Conclusions: This study provides the first evidence that shorter nighttime sleep duration has a linear association with higher energy intake early in life. That the effect is observed before emergence of associations between sleep and weight indicates that differences in energy intake may be a mechanism through which sleep influences weight gain

Inflammogenesis of Secondary Spinal Cord Injury

Spinal cord injury (SCI) and spinal infarction lead to neurological complications and eventually to paraplegia or quadriplegia. These extremely debilitating conditions are major contributors to morbidity. Our understanding of SCI has certainly increased during the last decade, but remains far from clear. SCI consists of two defined phases: the initial impact causes primary injury, which is followed by a prolonged secondary injury consisting of evolving sub-phases that may last for years. The underlying pathophysiological mechanisms driving this condition are complex. Derangement of the vasculature is a notable feature of the pathology of SCI. In particular, an important component of SCI is the ischemia-reperfusion injury (IRI) that leads to endothelial dysfunction and changes in vascular permeability. Indeed, together with endothelial cell damage and failure in homeostasis, ischemia reperfusion injury triggers full-blown inflammatory cascades arising from activation of residential innate immune cells (microglia and astrocytes) and infiltrating leukocytes (neutrophils and macrophages). These inflammatory cells release neurotoxins (proinflammatory cytokines and chemokines, free radicals, excitotoxic amino acids, nitric oxide (NO)), all of which partake in axonal and neuronal deficit. Therefore, our review considers the recent advances in SCI mechanisms, whereby it becomes clear that SCI is a heterogeneous condition. Hence, this leads towards evidence of a restorative approach based on monotherapy with multiple targets or combinatorial treatment. Moreover, from evaluation of the existing literature, it appears that there is an urgent requirement for multi-centered, randomized trials for a large patient population. These clinical studies would offer an opportunity in stratifying SCI patients at high risk and selecting appropriate, optimal therapeutic regimens for personalized medicine.Grant #NPRP 4-571-3-171 from the Qatar National Research Fund(a member of Qatar Foundation)

An Interactive Image Segmentation Method in Hand Gesture Recognition

In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy

Extraction of sEMG Signal in Upper Limb Rehabilitation Robot

The limb motor dysfunction caused by cerebral injury brings a heavy burden to the patients family and society. The scientific rehabilitation training helps a lot in the recovery of limb motor function. The treatment of nerve rehabilitation is a hard work. At present, it mainly relies on the hand operation of rehabilitation physician to take rehabilitation exercises. It limits the improvement of rehabilitation. The combination of rehabilitation medicine and robot technology improves the efficiency of rehabilitation training and ensures the strength of action training, which has created a new way for the research on new rehabilitation technology. With the interdisciplinary development and integration, the rehabilitation medicine and the rehabilitation robot has been further studied and explored. The sEMG signal is one of the most important data in the limb rehabilitation, especially in upper limb rehabilitation robot. In the development of rehabilitation robot, the extraction of sEMG signal in upper limb rehabilitation is investigated deeply. The paper focuses on the status of the extraction of sEMG signal. In the end, the development trend for the future is discussed