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Purpose: Military boots mileage is a main factor that can affect the risk of running injuries. The present study aims to evaluate the effect of three types of military boots mileage on knee muscular co-contraction during running. Methods: Fifteen healthy males received three pairs of new military boots. Participants wore these boots for more than 6 months. Electromyography activity of lower limb muscles during running at constant speed was recorded during pre- and post-intervention. Then, knee muscular co-contraction was calculated. Results: Results showed a significant increase during loading response (P=0.030, n² p=0.157) and push-off (P=0.008, n² p=0.302) phases for general knee co-contraction at post-test compared to the pre-test. Also, directed mediolateral knee co-contraction showed a significant increment during mid-stance (P=0.028, n² p=0.040) and push-off (P=0.039, n² p=0.115) phases at post-test compared to the pre-test. Conclusion: It can be concluded that knee joint instability while using polyurethane thermoplastic is more than polyurethane boots. Also, our results demonstrated that maintaining knee stability in the anterior-posterior direction while using polyurethane thermoplastic is better than in polyurethane boots. [ABSTRACT FROM AUTHOR]

Food intake and eating habits have a significant impact on people’s health. Widespread diseases, such as diabetes and obesity, are directly related to eating habits. Therefore, monitoring diet can be a substantial base for developing methods and services to promote healthy lifestyle and improve personal and national health economy. Studies have demonstrated that manual reporting of food intake is inaccurate and often impractical. Thus, several methods have been proposed to automate the process. This article reviews the most relevant and recent researches on automatic diet monitoring, discussing their strengths and weaknesses. In particular, the article reviews two approaches to this problem, accounting for most of the work in the area. The first approach is based on image analysis and aims at extracting information about food content automatically from food images. The second one relies on wearable sensors and has the detection of eating behaviours as its main goal. [ABSTRACT FROM PUBLISHER]

A balanced diet is the key to a healthy lifestyle and is crucial for preventing or dealing with many chronic diseases such as diabetes and obesity. Therefore, monitoring diet can be an effective way of improving people's health. However, manual reporting of food intake has been shown to be inaccurate and often impractical. This paper presents a new approach to food intake quantity estimation using image-based modeling. The modeling method consists of three steps: firstly, a short video of the food is taken by the user's smartphone. From such a video, six frames are selected based on the pictures' viewpoints as determined by the smartphone's orientation sensors. Secondly, the user marks one of the frames to seed an interactive segmentation algorithm. Segmentation is based on a Gaussian Mixture Model alongside the graph-cut algorithm. Finally, a customized image-based modeling algorithm generates a point-cloud to model the food. At the same time, a stochastic object-detection method locates a checkerboard used as size/ground reference. The modeling algorithm is optimized such that the use of six input images still results in an acceptable computation cost. In our evaluation procedure, we achieved an average accuracy of 92% on a test set that includes images of different kinds of pasta and bread, with an average processing time of about 23 s. [ABSTRACT FROM AUTHOR]