Thurdsak Leauhatong

Bio: Thurdsak Leauhatong is an academic researcher from King Mongkut's Institute of Technology Ladkrabang. The author has contributed to research in topics: Wavelet transform & Wavelet. The author has an hindex of 3, co-authored 7 publications receiving 51 citations.

Human falling detection algorithm using back propagation neural network

Abstract: A fall monitor system is necessary to reduce the rate of fall fatalities in elderly people. As an accelerometer has been smaller and inexpensive, it has been becoming widely used in motion detection fields. This paper proposes the falling detection algorithm based on back propagation neural network to detect the fall of elderly people. In the experiment, a tri-axial accelerometer was attached to waists of five healthy and young people. In order to evaluate the performance of the fall detection, five young people were asked to simulate four daily-life activities and four falls; walking, jumping, flopping on bed, rising from bed, front fall, back fall, left fall and right fall. The experimental results show that the proposed algorithm can potentially distinguish the falling activities from the other daily-life activities.

ECG analysis for person identification

Abstract: Electrocardiogram (ECG) has been actively proposed as aliveness biometric. In this paper, the study which concern to a realistic application is proposed. Firstly, a single lead normal ECG signal is acquired from individuals of 10 subjects. Then, each single beat ECG is segmented and analyzed in Continuous Wavelet Transform (CWT) domain. Total energy of wavelet coefficients for each P, QRS, and T segment is calculated. Next, the Fisher Linear Discriminant Analysis (FLDA) is applied. Finally, normalized Euclidean distance is implemented as a classifier. In experimental results, 97% of classification accuracy is achieved in case of a normal ECG (with non-variation of heart rate).

A new content-based medical image retrieval system based on wavelet transform and multidimensional Wald-Wolfowitz runs test

Abstract: Recently, one of the authors proposed a new similarity measure, called weighted multidimensional Wald and Wolfowitz (MWW) runs test, for the content-based color image retrieval system. The algorithm outperforms conventional similarity measures for comparing two color images. In this paper, we propose a new content-based medical image retrieval system based on discrete wavelet transform (DWT) symlet and the weighted MWW runs test. The DWT is used to extracted texture features of the medical images. The weighted MWW runs test is used to compare distributions of texture features of two medical images. Our experiments were performed on 1,000 medical images from image retrieval in medical applications (IRMA). The experimental results show promisingly efficient to retrieve the medical images.

A novel algorithm for detection human falling from accelerometer signal using wavelet transform and neural network

Abstract: Falls are major problems that could have happened to elderly, and could cause paralysis, hip fractures, or could lead to disabilities or accidental deaths. An algorithm for accurately detecting the falls is necessary in order to decrease the rate of disabilities or accidental deaths. In this paper, a new algorithm to detect the falls from the acceleration signal using the wavelet transform and multilayer perceptron neural network is proposed. In our experiments, 5 volunteers who were healthy with the ages between 21 to 25 year old were asked to attach a tri-axial accelerometer at the right side of their waists. The orientation of the accelerometer was vertical direction. Next, the volunteers were asked to perform 5 daily-life activities: 1) walking 2) standing up from a chair 3) sitting down on a chair 4) lying down on a bed and 5) getting up from a bed; and 5 falling activities: 1) falling forward 2) falling backward 3) falling to the right side 4) falling to the left side and 5) falling while standing up. The experimental results of the human activity classification that the proposed algorithm gave the maximum precision value (0.856). Moreover, it can be seen from the experiments of the falling detection that the proposed algorithm gave the maximum precision value (1.000)

Optimal selection of mother wavelet for classifying human activities from acceleration signals

Abstract: Falls are major problems that could have happened to elderly, and could cause paralysis, hip fractures, disabilities or accidental deaths. A human activity classification from acceleration signals may be an important process in fall prevention or detection. An algorithm which combines the wavelet transform and the multilayer perceptron neural network is an effective tool for classifying complicate signals. In order to optimize the classification, this paper aims to compare the performances of the algorithm which uses different mother wavelets. In our experiments, 5 volunteers who were healthy with the ages between 21 to 25 year old were asked to attach a tri-axial accelerometer at the right side of their waists. Next, the volunteers were asked to perform 5 daily-life activities: 1) walking, 2) standing up from a chair, 3) sitting down on a chair, 4) lying down on a bed, and 5) getting up from a bed; and 5 falling events: 1) forward falling, 2) backward falling, 3) falling to the right side, 4) falling to the left side, and 5) falling when standing up. In this paper, there are 2 experiments. In the first experiment, the algorithm was used to classify the real activity of the acceleration signals. Then the output of the algorithm can be any activity from ten activities. In the second experiment, the algorithm was used to detect the falling events. Then the output of the algorithm has 2 values; the falling event or the daily-life activity. The mother wavelets which an; used to evaluate the performances of the classification were Daubechies, Coiflet, Symlet, and Biorthogonal. From the experiments of the both of the human activity classification and the falling detection, the algorithm which used the Biorthogonal mother wavelet showed the best performances.

A Review on Video-Based Human Activity Recognition

Abstract: This review article surveys extensively the current progresses made toward video-based human activity recognition Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems

Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

Abstract: According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people's lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and they both strive for improving people's lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters.

Vision-Based Fall Detection with Convolutional Neural Networks

Abstract: One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems

Abstract: Falls are a major health problem for the frail community dwelling old people. For more than two decades, falls have been extensively investigated by medical institutions to mitigate their impact (e.g., lack of independence and fear of falling) and minimize their consequences (e.g., cost of hospitalization and so on). However, the problem of elderly falling does not only concern health-professionals but has also drawn the interest of the scientific community. In fact, falls have been the object of many research studies and the purpose of many commercial products from academia and industry. These studies have tackled the problem using fall detection approaches exhausting a variety of sensing methods. Lately, researcher has shifted their efforts to fall prevention where falls might be spotted before they even happen. Despite their restriction to clinical studies, early fall prediction systems have started to emerge. At the same time, current reviews in this field lack a common ground classification. In this context, the main contribution of this paper is to give a comprehensive overview on elderly falls and to propose a generic classification of fall-related systems based on their sensor deployment. An extensive research scheme from fall detection to fall prevention systems have also been conducted based on this common ground classification. Data processing techniques in both fall detection and fall prevention tracks are also highlighted. The objective of this paper is to deliver medical technologists in the field of public health a good position regarding fall-related systems.

Vision Based Human Activity Recognition: A Review

Abstract: Human activity recognition (HAR) is an important research area in computer vision due to its vast range of applications. Specifically, the past decade has witnessed enormous growth in its applications, such as Human Computer Interaction, intelligent video surveillance, ambient assisted living, entertainment, human-robot interaction, and intelligent transportation systems. This review paper provides a comprehensive state-of-the-art survey of different phases of HAR. Techniques related to segmentation of the image into physical objects, feature extraction, and activity classification are thoroughly reviewed and compared. Finally, the paper is concluded with research challenges and future directions.