[PDF] Intelligent Sensors For Human Motion Analysis eBook
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The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems.
This book introduces readers to the latest exciting advances in human motion sensing and recognition, from the theoretical development of fuzzy approaches to their applications. The topics covered include human motion recognition in 2D and 3D, hand motion analysis with contact sensors, and vision-based view-invariant motion recognition, especially from the perspective of Fuzzy Qualitative techniques. With the rapid development of technologies in microelectronics, computers, networks, and robotics over the last decade, increasing attention has been focused on human motion sensing and recognition in many emerging and active disciplines where human motions need to be automatically tracked, analyzed or understood, such as smart surveillance, intelligent human-computer interaction, robot motion learning, and interactive gaming. Current challenges mainly stem from the dynamic environment, data multi-modality, uncertain sensory information, and real-time issues. These techniques are shown to effectively address the above challenges by bridging the gap between symbolic cognitive functions and numerical sensing & control tasks in intelligent systems. The book not only serves as a valuable reference source for researchers and professionals in the fields of computer vision and robotics, but will also benefit practitioners and graduates/postgraduates seeking advanced information on fuzzy techniques and their applications in motion analysis.
The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application.
"This book highlights the development of robust and effective vision-based motion understanding systems, addressing specific vision applications such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval"--Provided by publisher.
It is with great pleasure that we present to you a collection of over 200 high quality technical papers from more than 10 countries that were presented at the Biomed 2008. The papers cover almost every aspect of Biomedical Engineering, from artificial intelligence to biomechanics, from medical informatics to tissue engineering. They also come from almost all parts of the globe, from America to Europe, from the Middle East to the Asia-Pacific. This set of papers presents to you the current research work being carried out in various disciplines of Biomedical En- neering, including new and innovative researches in emerging areas. As the organizers of Biomed 2008, we are very proud to be able to come-up with this publication. We owe the success to many individuals who worked very hard to achieve this: members of the Technical Committee, the Editors, and the Inter- tional Advisory Committee. We would like to take this opportunity to record our thanks and appreciation to each and every one of them. We are pretty sure that you will find many of the papers illuminating and useful for your own research and study. We hope that you will enjoy yourselves going through them as much as we had enjoyed compiling them into the proceedings. Assoc. Prof. Dr. Noor Azuan Abu Osman Chairperson, Organising Committee, Biomed 2008
High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary. In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson's disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user's itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system.
The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application.
HUMAN MOTION CAPTURE AND IDENTIFICATION FOR ASSISTIVE SYSTEMS DESIGN IN REHABILITATION A guide to the core ideas of human motion capture in a rapidly changing technological landscape Human Motion Capture and Identification for Assistive Systems Design in Rehabilitation aims to fill a gap in the literature by providing a link between sensing, data analytics, and signal processing through the characterisation of movements of clinical significance. As noted experts on the topic, the authors apply an application-focused approach in offering an essential guide that explores various affordable and readily available technologies for sensing human motion. The book attempts to offer a fundamental approach to the capture of human bio-kinematic motions for the purpose of uncovering diagnostic and severity assessment parameters of movement disorders. This is achieved through an analysis of the physiological reasoning behind such motions. Comprehensive in scope, the text also covers sensors and data capture and details their translation to different features of movement with clinical significance, thereby linking them in a seamless and cohesive form and introducing a new form of assistive device design literature. This important book: Offers a fundamental approach to bio-kinematic motions and the physiological reasoning behind such motions Includes information on sensors and data capture and explores their clinical significance Links sensors and data capture to parameters of interest to therapists and clinicians Addresses the need for a comprehensive coverage of human motion capture and identification for the purpose of diagnosis and severity assessment of movement disorders Written for academics, technologists, therapists, and clinicians focusing on human motion, Human Motion Capture and Identification for Assistive Systems Design in Rehabilitation provides a holistic view for assistive device design, optimizing various parameters of interest to relevant audiences.
Human motion analysis attempts to understand the movements of human body using techniques found in various disciplines. The movements of human body can be interpreted on a physical level through pose estimation, i.e., static reconstruction of three dimensional (3D) articulated configurations, or on a higher more semantic level through action recognition, i.e., understanding the body’s movements over time. It has a wide array of applications in the areas like gaming, sports analysis, security and surveillance, and healthcare. In the gaming industry, learning human action style and creating character animation from a repertoire of actions is very popular. Gait analysis is a crucial step in many biomedical applications as well as security, surveillance and biometric applications. A plethora of sensors are available to capture the human motion data in various modalities easily and in a very cost effective manner. The sheer amount of data produced by researchers, using such sensors, every day demand for human motion analysis methods that are computationally efficient. This thesis attempts to develop effective techniques, based on computer vision and computer graphics, to solve some of the important problems in application areas of human motion analysis. Specifically, three key application areas, namely, sports activity analysis, surveillance and security, and healthcare are considered. New methods for applications like human style sequence learning, gait analysis, gesture recognition, and time series matching are proposed. In the first half of this thesis, the problem addressed is learning and synthesis of structured sport activities with kickboxing as a case study. Monocular video data is used as input and human style sequences are investigated in order to identify higher level sequence style. Main idea is to learn the style embedded in action sequencing and transitions in between, and then to synthesize new sequences for virtual characters. Widely popular computer vision techniques are employed to obtain the sequence of actions performed in the video and to train a model with this sequence to drive a virtual character. Furthermore, style information embedded in transitions are also obtained from video and is used to create style preserving realistic transitions. In the second half of this thesis, high dimensional sensor data in different modalities is analyzed. Since human motion data is a high dimensional time series data, a novel shape aware multidimensional time series matching algorithm is developed and tested for a variety of scenarios like noise, missing data, different data modalities, small amount of training as well as testing data and different application domains. Firstly, processes for two new biometric systems, namely, gait and visual password are proposed towards surveillance and security. Secondly, processes for recognition of gestures and activities are proposed towards healthcare. Extensive experimentations are performed to demonstrate the effectiveness and validity of the various techniques developed in this thesis. Performance of the proposed methods is compared with that of the state of the art methods used in the human motion analysis under best possible conditions. For this purpose, the results obtained are validated using some popular benchmark databases as well as a few in-house created databases. It is shown that, the proposed methods outperform the state of the art in most of the cases.