In previous studies, we proposed a system for classifying gait based on step length and foot-ground clearance using arm acceleration. In the present study, we evaluated the application of machine learning to gait classification. The method was tested empirically on the classification of three gait patterns performed by 10 young and healthy participants. The three gait patterns were normal step, high step, and long step. Using measures of accuracy, precision, recall, and F-measure, we compared the performances of the following six classifiers: naive Bayes, support vector machine, neural network, logistic regression, instance-based classifier, and decision tree. The proposed method was shown to be capable of classifying the three gait patterns of seven participants with an accuracy greater than 0.6. This suggested that the proposed machine learning-based method is appropriate for its application in gait classification systems.

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This paper presents a mathematical model for forecasting the Mexico’s minimum wage using regressions. First, the Mexico’s minimum wage is investigated by EXCEL to observe the different trends. The trends studied are: exponential, linear, logarithmic, polynomial, power and moving average. The model that is adjusted to real conditions of the Mexico’s minimum wage is developed by regressions through the data analysis of 25 years from 1993 until 2017. According to the figures shown, it is clearly observed that the polynomial trend of fourth order is more accurate with respect to exponential, linear, logarithmic, power and moving average. Then, the Polynomial Regression Model of fourth order has been developed, because it is the most appropriate, and also it is adjusted to real conditions of the Mexico’s minimum wage, which is the main contribution of this paper.

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In this research, programmable electro-cardiogram measurement system is designed and constructed. The system applies the structure of virtual instrument built by software part, Labview, hardware part, DAQ card and some external circuits are applied in the development process. This research integrates different fields that include physiological signals, circuits, virtual instrument, and physiology. The functions of system include electrocardiogram display, data saving, histogram display, heartbeat display, irregular heart rhythms warning and remote measurement. Practical measurements have been carried out to demonstrate the capabilities of this measurement technique.

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This paper primarily proposes a method of implementing statistical shape model (SSM) of distal femur by automatically determining subject-specific femoral coordinate system (FCS). These have a wide range of applications in both biomechanical, 3D image analysis, knee model design and surgical planning. The main challenge in implementing SSM of distal femur as being complex in shape and a part of the whole femur, is to find correspondence across subjects automatically for the purpose of aligning training subjects to a reference coordinate space. While conventional correspondence is observer-dependent and time consuming, this study is intended to tackle these drawbacks by proposing a fully user-independent method to accurately extracting the FCS using 3D magnetic resonance (MR) images of isolated knee because of its popularity in knee surgery due to high spatial resolution. The proposed methods are based on morphological analysis of distal femoral bone in 3D MR image to locate the anatomical features automatically. Afterward an SSM has been constructed by utilizing the implemented FCSs in a set of volumetric MR images. Images were reconstructed and synthesized in each dimension from the model, and finally were evaluated by benchmarks index-generalization ability. In addition, possible application of the implemented FCS could be volumetric image (CT/MR) matching of distal femur, etc.

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