WAVELET BASED FEATURE EXTRACTOR AND ANN BASED CLASSIFIER FOR OPTIMAL ECG INTERPRETATION

The heart plays the most vital role of supplying nutrients and oxygen in any organism. Any abnormality in its function renders the body to many complications which may sometimes even lead to death. Hence, timely and early diagnosis of any abnormality is extremely important. Another requirement of the hour is the Automatic detection. Several techniques have been developed till date, but efficiency achieved so far leaves room for improvement. This paper also, presents a technique that aims at automatic detection of cardiac abnormality using an Artificial Neural Network. The detection is done on the basis of the wave shapes of different QRS complexes for different arrhythmias which are extracted from the ECG beats using Wavelet Transform. As the Daubechies wavelets are similar in shape to the QRS complex of the ECG, db4 has been used in the above context. The performance accuracies achieved for training, testing known data and unknown data have been found to be 99.7%, 99.2% and 96.2% respectively. The MIT-BIH database has been used for the present study and an altogether of seven different beats have been used for classification

Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks

تُعَدُّ أنظمة النعال الحسّاسة للحركة تقنية واعدة للعديد من التطبيقات في الرعاية الصحية والرياضة. حيث يمكن أن توفّر هذه الأنظمة معلومات قيّمة حول توزيع الضغط على القدم وأنماط المشي لأفراد مختلفين. ومع ذلك، فإن تصميم وتنفيذ مثل هذه الأنظمة يواجه العديد من التحديات، مثل اختيار الحسّاسات والمعايرة ومعالجة البيانات والتفسير. في هذه الدراسة، نقترح نظام نعل حساس باستخدام مقاومات استشعار القوى  لقياس الضغط المطبّق من القدم على مناطق مختلفة من النعل. يقوم هذا النظام بتصنيف أربعة أنواع من تشوهات القدم: طبيعي، مسطح، انحراف القدم إلى الداخل، وزيادة انحراف القدم إلى الخارج. تستخدم مرحلة التصنيف قيم الضغط الفرقية على نقاط الضغط كمدخلات لنموذج التغذية الأمامية للشبكات العصبية. تم جمع البيانات من 60 فرداً تم تشخيصهم بالحالات المدروسة. حقق تنفيذ التغذية الأمامية للشبكات العصبية دقة بنسبة 96.6٪ باستخدام 50٪ من المجموعة البيانية كبيانات تدريبية و 92.8٪ باستخدام 30٪ من البيانات التدريبية فقط. ويوضح المقارنة مع الأعمال ذات الصلة الأثر الإيجابي لاستخدام القيم الفرق لنقاط الضغط كمدخلات للشبكات العصبية مقارنة بالبيانات الأولية.Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data

The modeling of human sensation in virtual environments.

Ka Keung Caramon Lee.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 100-105).Abstracts in English and Chinese.Contents --- p.iiiList of Figures --- p.viList of Tables --- p.ixChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Related Work --- p.3Chapter 1.2.1 --- Empirical Psychophysical Equations --- p.3Chapter 1.2.2 --- Industry Standards --- p.4Chapter 1.2.3 --- Fuzzy Logic --- p.4Chapter 1.2.4 --- Neural Networks --- p.5Chapter 1.3 --- Organization of Thesis --- p.7Chapter 2 --- Experimental Design --- p.9Chapter 2.1 --- Human Motion Sense --- p.9Chapter 2.2 --- Full-Body Motion Virtual Reality System --- p.12Chapter 2.3 --- Human Sensation Measure --- p.15Chapter 2.4 --- Trajectory Segmentation --- p.16Chapter 3 --- Learning and Validation of Human Sensation Models --- p.22Chapter 3.1 --- Cascade Neural Networks --- p.23Chapter 3.1.1 --- Dynamic Mapping --- p.26Chapter 3.2 --- Experimental Trajectory Data --- p.26Chapter 3.3 --- Effect of Trajectory Segmentation --- p.31Chapter 3.4 --- Model Validation --- p.32Chapter 3.5 --- Similarity Measure --- p.33Chapter 3.6 --- Similarity Measure Results --- p.38Chapter 4 --- Input Reduction for Human Sensation Modeling --- p.40Chapter 4.1 --- Introduction --- p.40Chapter 4.2 --- Input Reduction --- p.41Chapter 4.3 --- Feature Extraction and Input Selection --- p.42Chapter 4.4 --- Feature Extraction Using Principal Component Analysis --- p.44Chapter 4.5 --- Independent Component Analysis --- p.48Chapter 4.5.1 --- Measure of Gaussianity --- p.50Chapter 4.5.2 --- The Fixed Point ICA Algorithm --- p.51Chapter 4.6 --- Input Reduction Using Independent Component Analysis --- p.52Chapter 4.6.1 --- ICA Without Dimension Reduction --- p.52Chapter 4.6.2 --- Feature Extraction Using ICA --- p.55Chapter 4.6.3 --- Input Selection Using ICA --- p.57Chapter 4.6.4 --- Applying Input Selection by ICA on the Furnace Data --- p.58Chapter 4.6.5 --- Applying Input Selection by ICA to Sensation Modeling --- p.65Chapter 4.6.6 --- Cross Verification of Selected Inputs --- p.70Chapter 4.7 --- Summary on Input Reduction for Human Sensation Modeling --- p.72Chapter 5 --- Stimulus Modification Based on Human Sensation --- p.74Chapter 5.1 --- Need for Stimulus Modification --- p.74Chapter 5.2 --- Sensation Grades --- p.75Chapter 5.3 --- Trajectory Modification Scheme --- p.77Chapter 5.4 --- Experiments --- p.80Chapter 6 --- Conclusion --- p.86Chapter 6.1 --- Contributions --- p.86Chapter 6.2 --- Future Work --- p.87Chapter A --- Platform Model --- p.88Chapter A.1 --- Inverse Kinematics --- p.90Chapter A.2 --- Forward Kinematics --- p.93Chapter A.3 --- Platform Dynamics --- p.99Bibliography --- p.10

Shared control for navigation and balance of a dynamically stable robot.

by Law Kwok Ho Cedric.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 106-112).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Related work --- p.4Chapter 1.3 --- Thesis overview --- p.5Chapter 2 --- Single wheel robot: Gyrover --- p.9Chapter 2.1 --- Background --- p.9Chapter 2.2 --- Robot concept --- p.11Chapter 2.3 --- System description --- p.14Chapter 2.4 --- Flywheel characteristics --- p.16Chapter 2.5 --- Control patterns --- p.20Chapter 3 --- Learning Control --- p.22Chapter 3.1 --- Motivation --- p.22Chapter 3.2 --- Cascade Neural Network with Kalman filtering --- p.24Chapter 3.3 --- Learning architecture --- p.27Chapter 3.4 --- Input space --- p.29Chapter 3.5 --- Model evaluation --- p.30Chapter 3.6 --- Training procedures --- p.35Chapter 4 --- Control Architecture --- p.38Chapter 4.1 --- Behavior-based approach --- p.38Chapter 4.1.1 --- Concept and applications --- p.39Chapter 4.1.2 --- Levels of competence --- p.44Chapter 4.2 --- Behavior-based control of Gyrover: architecture --- p.45Chapter 4.3 --- Behavior-based control of Gyrover: case studies --- p.50Chapter 4.3.1 --- Vertical balancing --- p.51Chapter 4.3.2 --- Tiltup motion --- p.52Chapter 4.4 --- Discussions --- p.53Chapter 5 --- Implement ation of Learning Control --- p.57Chapter 5.1 --- Validation --- p.57Chapter 5.1.1 --- Vertical balancing --- p.58Chapter 5.1.2 --- Tilt-up motion --- p.62Chapter 5.1.3 --- Discussions --- p.62Chapter 5.2 --- Implementation --- p.65Chapter 5.2.1 --- Vertical balanced motion --- p.65Chapter 5.2.2 --- Tilt-up motion --- p.68Chapter 5.3 --- Combined motion --- p.70Chapter 5.4 --- Discussions --- p.72Chapter 6 --- Shared Control --- p.74Chapter 6.1 --- Concept --- p.74Chapter 6.2 --- Schemes --- p.78Chapter 6.2.1 --- Switch mode --- p.79Chapter 6.2.2 --- Distributed mode --- p.79Chapter 6.2.3 --- Combined mode --- p.80Chapter 6.3 --- Shared control of Gyrover --- p.81Chapter 6.4 --- How to share --- p.83Chapter 6.5 --- Experimental study --- p.88Chapter 6.5.1 --- Heading control --- p.89Chapter 6.5.2 --- Straight path --- p.90Chapter 6.5.3 --- Circular path --- p.91Chapter 6.5.4 --- Point-to-point navigation --- p.94Chapter 6.6 --- Discussions --- p.95Chapter 7 --- Conclusion --- p.103Chapter 7.1 --- Contributions --- p.103Chapter 7.2 --- Future work --- p.10

Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment

Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment

Modeling Productivity losses Due to Change Orders

Change orders are an integral part of construction projects regardless of project size or complexity. Changes may cause interruption to the unchanged scope of work and working conditions and, if poorly managed, may be detrimental to project success. Many studies have been carried out to quantify the impact of change orders on construction labour productivity, with varying degrees of accuracy and variables considered. These studies reveal that quantifying loss of productivity due to change orders is not an easy task and requires a comprehensive and holistic method. There are several methods for quantifying loss of productivity, such as measured mile analysis (MMA) and the total cost method (TCM). Although measured mile analysis (MMA) is a well-known and widely accepted method for quantifying the cumulative impact of change orders on labour productivity, it is not readily applicable to many cases. In this research two models were developed to quantify losses arising from change orders. The first model does not account for the timing of change orders, but the second model considers the timing of change orders on labour productivity. Two models were developed and tested utilizing artificial neural networks and two sets of data collected by others in that field. The two datasets were statistically analyzed and preprocessed in order to transfer the data to normal distribution form and eliminate insignificant variables considered in their development. Using best subset regression, a total of seventeen variables were reduced to nine variables accordingly. Also, the study datasets were categorized into three types of timing periods; early change, normal change and late change to create the timing model. This was implemented to enable a comparison with models developed by others. Three types of artificial neural network techniques were experimented with and evaluated for possible use in the developed models. These three types are Feed Forward Neural Network, Cascade Neural Network, and Generalized Regression Neural Network. Candidate techniques were evaluated and analyzed by neural network parameters and analysis of variance (ANOVA) to select the most efficient type of neural networks, and subsequently using it to develop two models; one considers timing and the second does not. The analysis performed led to the selection of the cascade neural network for the development of the two models productivity losses due to change orders. The developed models were tested and validated utilizing several actual cases reported by others. The models were applied to a number of cases and the results were compared to those generated by frequently cited models to demonstrate their accuracy. The comparison outcome showed that the developed models can generate more accurate and satisfactory results than those of reported in previous studies

CUHK electronic theses & dissertations collection

After capturing the object, the space robot must complete the following two tasks: one is to berth the object, and the other is to re-orientate the attitude of the whole robot system for communication and power supply. Therefore, I propose a method to accomplish these two tasks simultaneously using manipulator motion only.Finally I propose a novel approach based on Genetic Algorithms (GAs) to optimize the approach trajectory of space robots in order to realize effective and stable operations. I complete the minimum-torque path planning in order to save the limited energy in space, and design the minimum jerk trajectory for the stabilization of the space manipulator and its space base. These optimal algorithms are very important and useful for the application of space robot.In this thesis, I study and analyze the dynamics and control problems of space robot for capturing objects. This work has potential impact in space robotic applications. I first study the contact and impact dynamics of space robot and objects. I specifically focus on analyzing the impact dynamics and mapping the relationship of influence and speed. Then, I develop the fundamental theory for planning the minimum-collision based trajectory of space robot and designing the configuration of space robot at the moment of capture.Space robots are expected to perform intricate tasks in future space services, such as satellite maintenance, refueling, and replacing the orbital replacement unit (ORU). To realize these missions, the capturing operation may not be avoided. Such operations will encounter some challenges because space robots have some unique characteristics unfound on ground-based robots, such as, dynamic singularities, dynamic coupling between manipulator and space base, limited energy supply and working without a fixed base, and so on. In addition, since contacts and impacts may not be avoided during capturing operation. Therefore, dynamics and control problems of space robot for capturing objects are significant research topics if the robots are to be deployed for the space services. A typical servicing operation mainly includes three phases: capturing the object, berthing and docking the object, then repairing the target. Therefore, this thesis will focus on resolving some challenging problems during capturing the object, berthing and docking, and so on.The ultimate goal of space services is to realize the capture and manipulation autonomously. Therefore, I propose an affective approach based on learning human skill to track and capture the objects automatically in space. With human-teaching demonstration, the space robot is able to learn and abstract human tracking and capturing skill using an efficient neural-network learning architecture that combines flexible Cascade Neural Networks with Node Decoupled Extended Kalman Filtering (CNN-NDEKF). The simulation results attest that this approach is useful and feasible in tracking trajectory planning and capturing of space robot.To compensate for the attitude of the space base during the capturing approach operation, a new balance control concept which can effectively balance the attitude of the space base using the dynamic couplings is developed. The developed balance control concept helps to understand of the nature of space dynamic coupling, and can be readily applied to compensate or minimize the disturbance to the space base.Huang Panfeng."December 2005."Adviser: Yang Sheng Xu.Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6693.Thesis (Ph.D.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (p. 133-143).Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.Abstracts in English and Chinese.School code: 1307

CUHK electronic theses & dissertations collection

Assessment of different gait patterns of daily living could provides useful information in studying one individual's stability and mobility during locomotion. As the foundation for better assessment of different gait patterns, the ability to automatically identity different patterns and walking surroundings provide valuable information for further understanding the relations between gait pattern and energy consumption. We apply Discrete Wavelet Transform (DWT) for feature generation and Fuzzy-logic based approach for designing the multi-class classifier to identify gait patterns among fiat walking, descending stairs, and ascending stairs based on continuous kinematic signals.Falls in the aging population has always been one of the most challenging problems in public health care. We propose an automatic falling detection algorithm based on the analysis of plantar force on both feet, because plantar forces are an important parameters directly associated with postures of human locomotion. The proposed two-stage algorithm efficiently overcome the shortcomings of the widely proposed accelerometer or gyroscope based algorithms and could provide efficient assistant for automatic detection of falls once they occur.Finally, the research of studying gait abnormalities is introduced. We develop the methodology for modeling and classifying abnormal gaits including toe-in, toe-out, over-supination, and heel walking via machine learning algorithms, hidden Markov models (HMM) and support vector machine (SVM) based on a suite of gait parameters. The trained classifiers can classify abnormal gait patterns mentioned above and the proposed methodology will make it possible to provide realtime feedback to assist persons with gait abnormalities in the development of a normal walking pattern in their daily life.Keeping abnormal motion for long time will ultimately lead to pain in the feet, ankles, legs and skeletal disease, and badly influences the skelecton growth especially for children and adolescents. In biomedicine, gait analysis has been proved as an useful approach. in revealing helpful insights into the recognition of motion abnormalities. Analysis of gait is commonly used as a routine procedure in identifying movement or posture related abnormalities of humans and aiding the therapeutic processes. Our goal is to monitor and study gaits of humans in order that proper motion adjustments can he advised to improve their posture style and long-term well being.Most currently utilized measurement systems for motion and gait analysis have the shortcomings of that the monitoring and analysis of motion is constrained in a limited environment and human-related assistance is essential. All of them cannot be acceptable for the purpose of long-term monitoring and studying of motion abnormalities. In this thesis, a new concept of an inexpensive, compact, and lightweight shoe-integrated platform is introduced. The shoe-integrated system is composed of a suite of sensors for wirelessly capturing gait parameters and generating well qualified analysis results. The ideal platform requires no specialized equipment or lab setup, allowing data to be collected not only in the narrow confines of a research lab, but essentially anywhere, both indoors and outdoors.To be one of the common postural abnormalities, postural kyphosis is studied and modeled. We apply Cascade Neural Networks with Node-Decoupled Extended Kalman Filtering (CNN-NDEKF) to train the model for this binary classification problem. This proposed study is of particular significance to provide feedback in the application of postural kyphosis rectification.Chen, Meng."December 2009."Adviser: Yangsheng Xu.Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: .Thesis (Ph.D.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (leaves 120-130).Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.Abstract also in Chinese