XBee wireless sensor networks for heart rate monitoring in sport training

Heart Rate Monitors (HRMs) have become widely used since the last two decades. It used as training aid for various types of sports. And the development of new HRMs has evolved rapidly. Thus, in order to determine the exercise intensity of training session or race, HRMs are mainly used. Compared to the other indication of exercise intensity, Heart rate is easy to monitor, compatible to use in most situation and relatively cheap. Thus, it is most beneficial if wireless sensor network can be implementing into the system. Other than monitored their condition by themselves, this system may allowed a number of athletes being monitored simultaneously. Arduino-Nano board was used to interface with nRF24AP1 and XBee. The wireless sensor network consists of a microcontroller on Arduino-Nano board, nRF24AP1 and the XBee wireless communication module which is based on the IEEE 802.15.4. This system will involve peer to peer communication of ANT+ and mesh networking among the XBee

Midrange exploration exploitation searching particle swarm optimization in dynamic environment

Conventional Particle Swarm Optimization was introduced as an optimization technique for real problems such as scheduling, tracking, and traveling salesman. However, conventional Particle Swarm Optimization still has limitations in finding the optimal solution in a dynamic environment. Therefore, we proposed a new enhancement method of conventional Particle Swarm Optimization called Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO). The main objective of this improvement is to enhance the searching ability of poor particles in finding the best solution in dynamic problems. In MEESPSO, we still applied the basic process in conventional Particle Swarm Optimization such as initialization of particle location, population evolution, and updating particle location. However, we added some enhancement processes in MEESPSO such as updating the location of new poor particles based on the average value of the particle minimum fitness and maximum fitness. To see the performance of the proposed method, we compare the proposed method with three existing methods such as Conventional Particle Swarm Optimization, Differential Evaluation Particle Swarm Optimization, and Global Best Local Neighborhood Particle Swarm Optimization. Based on the experimental result of 50 datasets show that MEESPSO can find the quality solution in term of number of particle and iteration, consistency, convergence, optimum value, and error rate

Video Tracking System Using Midrange Exploration Exploitation Searching-Particle Swarm Optimization (MEESPSO) in handling occlusion and similar appearance due to crowded environment

Detecting the correct object plays a key role in generating an accurate and precise object tracking result. In addition, the usage of conventional method still brings the limitation in term of the accuracy and precision of the detected object. Besides, the process of object tracking in an individual frame is also challenging due to the problems such as occlusion, crowded environment, and similar appearance Therefore, a Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) algorithm with color-shape feature pattern matching methods was introducing to address the problem of the similar appearance or color that comes close to target object in crowded environment, and the presence of occlusion problem cause motion of the crowded object or the camera views. The proposed method is tested by using the MOT16-11 benchmark video dataset. This benchmark video faced the challenges such as partial occlusion, fully occlusion and similar appearance due to crowded environment in the video scene. The experiment has shown that the tracking performance of the proposed method has increased more than 92.69% accuracy and 94.67% precisio

An improved pheromone-based kohonen self-organising map in clustering and visualising balanced and imbalanced datasets

The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. The Kohonen Self-Organising Map (KSOM) is one of the well-known clustering algorithms that can solve various problems without a pre-defined number of clusters. However, similar to other clustering algorithms, this algorithm requires sufficient data for its unsupervised learning process. The inadequate amount of class label data in a dataset significantly affects the clustering learning process, leading to inefficient and unreliable results. Numerous research have been conducted by hybridising and optimising the KSOM algorithm with various optimisation techniques. Unfortunately, the problems are still unsolved, especially separation boundary and overlapping clusters. Therefore, this research proposed an improved pheromone- based PKSOM algorithm known as iPKSOM to solve the mentioned problem. Six different datasets, i.e. Iris, Seed, Glass, Titanic, WDBC, and Tropical Wood datasets were chosen to investigate the effectiveness of the iPKSOM algorithm. All datasets were observed and compared with the original KSOM results. This modification significantly impacted the clustering process by improving and refining the scatteredness of clustering data and reducing overlapping clusters. Therefore, this proposed algorithm can be implemented in clustering other complex datasets, such as high dimensional and streaming data

MMZ: A Study on the Implementation of Mathematical Game-based Learning Tool

Mathematic has always been one of the hardest subject to be learnt during school. This is the very same issue that student have been facing no matter the level of education that they are in, and this is the reason why Math Maze Zone or also known as MMZ had been developed. MMZ is a mathematical game-based learning tool for primary school students to help them prepare for their final examination for the mathematics subject. The proposed game will consist of mathematical questions and the game design will be focusing on a maze where user will have to search for a way out from the maze while going through the checkpoint within the maze. The checkpoint will consist of mathematical questions and when user answered correctly, they will be able to continue their journey to explore the maze. MMZ focused on 1 chapter only for now which is Chapter 8: Space and Shape. Although the game only consists of one-chapter, primary school students can play the game to enhance their knowledge and to help them to be more engaged with mathematics subject by playing the game. The results will be taken from the perspective of adult who have close relationship with standard six students. Five main sections will also be identified along with MMZ to ensure getting a good result

ELA REMIND : A medicine box reminder system for elderly living alone (ELA) implementing the infrared sensor, internet of things and mobile application

Studies had shown a high statistic of the elderly often forgetting to take their medicine each day due to their forgetfulness. And living alone far from their family, make things harder. As a human of 60 years old and above, they might have difficulties to remember and become alert of the medicine time. Thus, developing this ELA Remind which is a medicine box reminder system for Elderly Living Alone (ELA) by implementing the Infrared Sensor, Internet of Things and Mobile Application. By having this system, elderly will always be reminded on time to take their medicine every day without fail. In fact, it helps to check on their medication intake history to ensure that the elderly was taking the medicine as per schedule. At the same time, it helps reduce the wastage of medication as well. Besides, the most impactful element in this system is that the reminder will also be sent to the elderly's families so that they can monitor their elders remotely through the mobile application. In addition, this system will enable users or families to check back on the history of time taken of the medicine whenever needed

Development of an integrated control system for rescue boat

During flood season, fire department or rescuing team are facing the situation which is lack of man power. To overcome this problem, an integrated rescuing boat control system has been proposed. The developed rescuing boat controller is convenience and easy to handle. It has been built with an integrated system which could be handled manually or automatically. The controller is developed using microcontroller Arduino. For an autonomous mode, some sensors has been introduced to sense the obstacles and giving the instruction to the Arduino to changed the direction of the boat. For manual control, rescuing boat could be controlled by Blynk application through smart phone. By introcuding an integrated rescuing boat control system, the victim can save their own from dangerous and reduce dramatically the number of death or injuries due to the flood

Internet of Things (IoT) Based Fire Alert Monitoring System for Car Parking

Safety is one of the important factors that should be considered either in the parking area, workplace, home area and so forth. In the university parking area, the students are unable to receive any information regarding a fire smoke or an accident near their vehicle. In addition, the parking safety also not assured due to the shortage of car superintendence and there is no any strict parking management by the security officer. Therefore, a fire smoke alert monitoring system in the university parking area is necessary in order to prevent any accidents that may cause property breakdown and loss of life as happens inside the university area. This system should be introduced since the existing parking is unsystematic and less efficient as it unable to response the complications that are regularly happen to the students because they do not receive any information regarding a fire smoke or an accident near their vehicle in the parking area. With this new system, a few advancements are implemented in order to help the students in various aspects by using multiple and distinct Arduino devices. Moreover, an android application is developed to facilitate the security officer in order to identify the car information that are involved in the accident that might be occur in the university parking area

Real-time threshold-based fall detection system using wearable IoT

This paper presents a Real-Time Fall Detection System (FDS) in the form of a wearable device integrating an ADXL335 accelerometer as a fall detection sensor, and classify the falling condition based on the threshold method. This system detects the wearer's movements and analyses the result in binary output conditions of 'Fall' for any fall occurrence or 'Normal' for other activities. The transmitter or FDS-Tx which is attached to the user's garment will constantly transmit data reading to the receiver or FDS-Rx via XBee module for data analysis. Raspberry Pi as the processor in FDS-Rx provides computational resources for immediate output analysis, by using threshold method, the computed results are sent to the cloud utilizing the Wi-Fi to display the user's condition on the authority's dashboard for further action. The working conditions of the systems are validated through an experiment of 10 volunteers whose perform several activities including fall events. Based on the threshold proposed, the results showed 97% sensitivity, 69% specificity and 83% accuracy from the experiment. Thus, this system fulfilled the real-Time working condition integrating (IoT) as accordingly