Abstract:A new, continuously-monitoring portable device that monitors the diabetic foot has shown to help in reduction of diabetic foot complications. Persons affected by diabetic foot have shown to be particularly sensitive in the plantar surface; this sensitivity coupled with certain ambient conditions may cause dry skin. This dry skin leads to the formation of fissures that may eventually result in a foot ulceration and subsequent hospitalization. This new device monitors the micro-climate temperature and humidity areas between the insole and sole of the footwear. The monitoring system consists of an array of ten sensors that take readings of relative humidity within the range of 100%˘2% and temperature within the range of´40˝C to 123.8˘0.3˝C. Continuous data is collected using embedded C software and the recorded data is processed in Matlab. This allows for the display of data; the implementation of the iterative Gauss-Newton algorithm method was used to display an exponential response curve. Therefore, the aim of our system is to obtain feedback data and provide the critical information to various footwear manufacturers. The footwear manufactures will utilize this critical information to design and manufacture diabetic footwear that reduce the risk of ulcers in diabetic feet.
The management of the uncertainty existing in any production system is fundamental to define machine scheduling models that allow programming production instances attached to the real world. In this research, a generalized decision-making system is developed for the management of uncertainty existing in flow shop machine scheduling models. The system assessment the uncertainty existing in internal and external factors that influence the decision-making process of production programming experts, and that is decisive in a final machine scheduling. The system is based on the combination of the Fuzzy Hierarchical Analysis Process, a membership analysis, and an Artificial Neural Network (ANN). The system allows to concentrate the experience of experts in machine scheduling and generalize their knowledge. The efficiency of the system is verified with a Fuzzy Hierarchical Analysis Process Model, the "ANN toolbox" preloaded in MATLAB and variety of structures of an Artificial Neural Network. The results are validated in an industrial application and the system is contrasted against an expert. The results show the efficiency of the system as it defines and predicts the final machine scheduling of production instances; the joint assessment of variables that add uncertainty to the production system allowed to reduce delays in product deliveries.
Achieving the highest levels of repeatability and precision, especially in robot manipulators applied in automation manufacturing, is a practical pose-recognition problem in robotics. Deviations from nominal robot geometry could produce substantial errors at the end effector, which can be more than 0.5 inches for a 6 ft robot arm. In this research, a pose-recognition system is developed for estimating the position of each robot joint and end-effector pose using image processing. To generate the joint angle, the system is developed via the modeling of a pose obtained by combining a convolutional neural network (CNN) and a multi-layer perceptron network (MLP). The CNN categorizes the input image generated by a remote monocular camera and generates a classification probability vector. The MLP generates a multiple linear regression model based on the probability vector generated by a CNN and describes the values of each joint angle. The proposed model is compared with the P-n-Perspective problem-solving method, which is based on marker tracking using ArUco markers and the encoder values. The system was verified using a robot manipulator with four degrees of freedom. Additionally, the proposed method exhibits superior performance in terms of joint-by-joint error, with an absolute error that is three units less than that of the computer vision method. Furthermore, when evaluating the end-effector pose, the proposed method showed a lower average standard deviation of 9mm compared with the computer vision method, which had a standard deviation of 13 mm.
Telemetry home, mobile technologies, and software application, has a positive effect onthe monitoring and management of some medical control parameters, and help to be more effective in the treatments, measuring blood pressure at home is becoming increasingly use by both clinicians and patients. This work implements a way to obtain information directly from commercial blood pressure devices,using application programming interface, without to have used the proprietary APP of the brand, use of the optical character recognition and the use of gestures using the fingers to write each of the blood pressure values; systole, diastole, pressure, date and time and send these data to a Web-DataBase way SMS message
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