IoT based real-time remote patient monitoring system

Healthcare technology is one of the most popular studies nowadays. With the development of healthcare technology, the lifespan of people has successfully extended. However, people in the rural area are still having a hard time to obtain professional healthcare services due to the barrier of distance and lack of doctors. A remote patient monitoring system is one of the best solutions to overcome this issue. This paper proposes an Internet of Things (IoT) based real-time remote patient monitoring system that is able to guarantee the integrity of the real-time electrocardiogram (ECG). Message Queuing Telemetry Transport (MQTT) protocol is used for transmitting the real-time ECG from the proposed system to the webserver. The doctor can access the webserver via smartphone or computer to monitor the real-time or previously recorded ECG data. The proposed system has been tested in both Local Area Network and Wide Area Network environments. The results show that the proposed system has no package loss and packet error in both Local Area Network and Wide Area Network

Investigation of Data Mining Using Pruned Artificial Neural Network Tree

A major drawback associated with the use of artificial neural networks for data mining is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the knowledge captured is not transparent and cannot be verified by domain experts. In this paper, Artificial Neural Network Tree (ANNT), i.e. ANN training preceded by Decision Tree rules extraction method is presented to overcome the comprehensibility problem of ANN. Two pruning techniques are used with the ANNT algorithm; one is to prune the neural network and another to prune the tree. Both of these pruning methods are evaluated to see the effect on ANNT in terms of accuracy, comprehensibility and fidelity

Person identification using gait

In this paper, Principal Component Analysis (PCA) with and without Radon Transform (RT) are applied for gait recognition purposes. The Radon Transform is used to detect features within an image and PCA is used to the reduce dimension of the images without much loss of information. The side view of slow walk, fast walk and carrying a ball walk have been selected from the CMU MoBo database for experimental purposes. The two techniques experimental result achieved equal recognition rates (EER) of 85.40%, 78.07% and 90.05% for RT with PCA and 85.18%, 80%, and 89.90% for PCA only for slow walk, fast walk and carrying a ball walk respectively

Path planning of UAV based on fluid computing via accelerated method

This paper presents our study on the method for planning the path for low-flying unmanned aerial vehicle (UAV) in complex terrain based on the theory of fluid flow. First, the 2D terrain map is generated using hill algorithm. Then the fluid field distribution is computed using fluid mechanics to establish streamlines in the field. These streamlines are then used as flight paths for UAV. In fluid mechanics, Laplace's equation is used as the controlling equation of the potential flow. The solutions of Laplace's equation can be solved using finite difference method. Existing methods are slow in computing the solutions for generating UAVs flight path. Hence, they are not suitable for real-time processing. In this paper, an efficient iterative method namely Two-Parameter Overrelaxation (TOR) method that employ an acceleration parameter is proposed. The efficiency of this approach is shown by comparing its performance with the previous methods. It was shown that the proposed TOR method outperformed the previous methods

A learning system prediction method using fuzzy regression

Palmprint identification is the measurement of palmprint features for recognizing the identity of a user. Palmprint is universal, easy to capture and does not change much across time. Palmprint biometric system does not requires specialized acquisition devices. It is user-friendly and more acceptable by the public. Besides that, palmprint contains different types of features, such as geometry features, line features, point features, statistical features and texture features. In this work, peg-less right hand images for 100 different individuals were acquired ten times. No special lighting is used in this setup. The hand image is segmented and its key points are located. The hand image is aligned and cropped according to the key points. The palmprint image is enhanced and resized. Sequential modified Haar transform [1] is applied to the resized palmprint image to obtain Modified Haar Energy (MHE) feature. The sequential modified Haar wavelet can maps the integer-valued signals onto integer-valued signals without abandoning the property of perfect reconstruction. The MHE feature is compared with the feature vectors stored in the database using Euclidean Distance. The accuracy of the MHE feature and Haar energy feature under different decomposition levels and combinations are compared. 94.3678 percent accuracy can be achieved using proposed MHE feature

Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment

Generating path planning for unmanned aerial vehicles (UAV) is important to provide a smooth navigation flight path from source to destination. In the past, fast iterative methods that apply the use of full-sweep iteration were suggested. In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. The proposed method was tested using several simulation scenarios which demonstrated the efficiency of the algorithm by finding the path in term of smoothness, computational time efficiency and number of iterations, with a different number of outdoor static obstacles in the form of hills. The results show that RSOR gives a faster computational time and less iterations to generate a path for UAV platform when compared to previous methods

Multimodal biometrics based on identification and varification system

The need for an increase of reliability and security in a biometric system is motivated by the fact that there is no single technology that can realize multi-purpose scenarios. Experimental results showed that the recognition rate of Heart Sound Identification (HSI) model is 81.9%, while the rate for Speaker Identification (SI) model is 99.3% from 20 clients and 70 impostors. Heart Sound-Verification (HSV) provides an average Equal Error Rate (EER) of 13.8%, while the average EER for the Speaker Verification model (SV) is 2.1%. Electrocardiogram Identification (ECGI), on the other hand, provides an accuracy of 98.5% and ECG Verification (ECGV) EER of 4.5%. In order to reach a higher security level, an alternative multimodal and a fusion technique were implemented into the system. Through the performance analysis of the three biometric system and their combination using two multimodal biometric score level fusion, this paper found the optimal combination of those systems. The best performance of the work is based on simple-sum score fusion, with a piecewise-linear normalization technique which provides an EER of 0.7%