Purpose The recent advancement in gait analysis combines internet of things that provides better observations of person living behavior. The biomechanical model used for elderly and physically challenged persons is related to gait-related parameters, and the accuracy of the existing systems significantly varies according to different person abilities and their challenges. The paper aims to discuss these issues. Design/methodology/approach Deployment of wearable sensors in gait analysis provides a better solution while tracking the changes of the personal style, and this proposed model uses an electronics system using force sensing resistor and body sensors. Findings Experimental results provide an average gait recognition of 95 percent compared to the existing neural network-based gait analysis model based on the walking speeds and threshold values. Originality/value The sensors are used to monitor and update the predicted values of a person for analysis. Using IoT a communication process is performed in the research work by identifying a physically challenged person even in crowded areas.

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Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.

ECG data compression algorithms are important for storage, transmission and analysis. An essential requirement of the compression algorithms is that the significant morphological features of the signal should not be lost upon reconstruction. In this paper two different neural network based methods are investigated for ECG data compression. The first method uses filters for attenuating noise and interferences, a Radial-Basis Function network for the detection of R-points for separating the waveform into different cycles and finally Multilayer Back propagation networks for data compression. In the second method, the back propagation networks are used as nonlinear predictors for achieving the data compression. Compression results obtained by using the two different methods are evaluated based on standard MIT-BlH ECG Test Database.