Using Type-2 Fuzzy Models to Detect Fall Incidents and Abnormal Gaits Among Elderly

— June 2012, 11% of the overall population in Taiwan was over the age of 65. This ratio is higher than the average figure for the United Nations (8%) . Critical issues concerning elderly in healthcare include fall detection, loneliness prevention and retard of obliviousness. In this study we design type-2 fuzzy models that utilize smart phone tri-axial accelerometer signals to detect fall incidents and identify abnormal gaits among elderly. Once a fall incident is detected an alarm is sent to notify the medical staff for taking any necessary treatment. When the proposed system is used as a pedometer, all the tri-axial accelerometer signals are used to identify the gaits during walking. Based on the proposed type-2 fuzzy models, the walking gaits can be identified as normal, left-tilted, and right-tilted. Experimental results from type-2 fuzzy models reveal that the accuracy rates in identifying normal walking and fall over are 92.3% and 100%, respectively, exceeding what are obtained using type-1 fuzzy models

Machine Learning-based Detection of Compensatory Balance Responses and Environmental Fall Risks Using Wearable Sensors

Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide, with serious and costly consequences. Compensatory balance responses (CBRs) are reactions to recover stability following a loss of balance, potentially resulting in a fall if sufficient recovery mechanisms are not activated. While performance of CBRs are demonstrated risk factors for falls in seniors, the frequency, type, and underlying cause of these incidents occurring in everyday life have not been well investigated. This study was spawned from the lack of research on development of fall risk assessment methods that can be used for continuous and long-term mobility monitoring of the geri- atric population, during activities of daily living, and in their dwellings. Wearable sensor systems (WSS) offer a promising approach for continuous real-time detection of gait and balance behavior to assess the risk of falling during activities of daily living. To detect CBRs, we record movement signals (e.g. acceleration) and activity patterns of four muscles involving in maintaining balance using wearable inertial measurement units (IMUs) and surface electromyography (sEMG) sensors. To develop more robust detection methods, we investigate machine learning approaches (e.g., support vector machines, neural networks) and successfully detect lateral CBRs, during normal gait with accuracies of 92.4% and 98.1% using sEMG and IMU signals, respectively. Moreover, to detect environmental fall-related hazards that are associated with CBRs, and affect balance control behavior of seniors, we employ an egocentric mobile vision system mounted on participants chest. Two algorithms (e.g. Gabor Barcodes and Convolutional Neural Networks) are developed. Our vision-based method detects 17 different classes of environmental risk factors (e.g., stairs, ramps, curbs) with 88.5% accuracy. To the best of the authors knowledge, this study is the first to develop and evaluate an automated vision-based method for fall hazard detection

Human Motion Analysis with Wearable Inertial Sensors

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

NOIRS 2008

"The National Institute for Occupational Safety and Health (NIOSH) hosted the fourth National Occupational Injury Research Symposium (NOIRS) on October 21-23, 2008 at the Sheraton Station Square in Pittsburgh, Pennsylvania. NOIRS is the only forum for the presentation of occupational injury research findings, data, and methods. This symposium served numerous objectives aimed at preventing traumatic occupational injury through research and prevention. They included: 1. Presenting current research findings. 2. Fostering collaboration among researchers from a broad range of disciplines and perspectives, and exploring underutilized disciplines and topic areas. 3. Identifying best practices in the area of intervention evaluation. 4. Exploring the cost-effectiveness of injury prevention strategies and interventions. 5. Showcasing innovative and high technology approaches to research and prevention. 6. Continuing to promote the implementation of NORA. Questions that were addressed included: 1. What are the latest traumatic occupational injury research findings? 2. What are emerging problem areas in workplace trauma? 3. How is prevention through design being applied to occupational injury research and prevention? 4. What activities are being done to implement research to practice in the area of traumatic occupational injury? 5. What are the best practice intervention and prevention strategies and which strategies do not work? In what specific workplaces and under what circumstances? 6. What are the economic costs of traumatic occupational injuries and how cost-effective are the prevention strategies? 7. What are current and emerging research areas and disciplines? 8. What are the trends in traumatic occupational injury and fatality incidence? In research tools, techniques, and methods? In prevention? 9. What specific workplace risks are faced by adolescents, older adults, minority workers, non-English-speaking workers, low-literacy workers, and other special populations? 10. How can researchers and practitioners in different sectors and disciplines better collaborate and coordinate their activities to reduce traumatic occupational injuries? 11. What methods are available to assess, quantify, and compare traumatic occupational injury risks? Occupational injury researchers from all disciplines were invited to attend and share their research. We encouraged participation by all interested individuals, including: Safety researchers; Safety practitioners; Health care professionals; Administrators; Epidemiologists; Engineers; Manufacturers; Communication Researchers; Health and science communicators; Regulators; Employers; Policy makers; Insurers; Students; Advocates; Workers; Educators and trainers; and, Others interested in attending. The symposium consisted of contributed oral presentations in concurrent sessions and a poster session." - NIOSHTIC-2Available via the World Wide Web as an Acrobat .pdf file (1.30 MB, 92 p.)

Using Type-2 Fuzzy Models to Detect Fall Incidents and Abnormal Gaits Among Elderly

— June 2012, 11% of the overall population in Taiwan was over the age of 65. This ratio is higher than the average figure for the United Nations (8%) . Critical issues concerning elderly in healthcare include fall detection, loneliness prevention and retard of obliviousness. In this study we design type-2 fuzzy models that utilize smart phone tri-axial accelerometer signals to detect fall incidents and identify abnormal gaits among elderly. Once a fall incident is detected an alarm is sent to notify the medical staff for taking any necessary treatment. When the proposed system is used as a pedometer, all the tri-axial accelerometer signals are used to identify the gaits during walking. Based on the proposed type-2 fuzzy models, the walking gaits can be identified as normal, left-tilted, and right-tilted. Experimental results from type-2 fuzzy models reveal that the accuracy rates in identifying normal walking and fall over are 92.3% and 100%, respectively, exceeding what are obtained using type-1 fuzzy models

Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

Investigation of mobile devices usage and mobile augmented reality applications among older people

Mobile devices such as tablets and smartphones have allow users to communicate, entertainment, access information and perform productivity. However, older people are having issues to utilise mobile devices that may affect their quality of life and wellbeing. There are some potentials of mobile Augmented Reality (AR) applications to increase older users mobile usage by enhancing their experience and learning. The study aims to investigate mobile devices potential barriers and influence factors in using mobile devices. It also seeks to understand older people issues in using AR applications

14th Conference on Dynamical Systems Theory and Applications DSTA 2017 ABSTRACTS

From Preface: This is the fourteen time when the conference “Dynamical Systems – Theory and Applications” gathers a numerous group of outstanding scientists and engineers, who deal with widely understood problems of theoretical and applied dynamics. Organization of the conference would not have been possible without a great effort of the staff of the Department of Automation, Biomechanics and Mechatronics. The patronage over the conference has been taken by the Committee of Mechanics of the Polish Academy of Sciences and the Ministry of Science and Higher Education. It is a great pleasure that our invitation has been accepted by so many people, including good colleagues and friends as well as a large group of researchers and scientists, who decided to participate in the conference for the first time. With proud and satisfaction we welcome nearly 250 persons from 38 countries all over the world. They decided to share the results of their research and many years experiences in the discipline of dynamical systems by submitting many very interesting papers. This booklet contains a collection of 375 abstracts, which have gained the acceptance of referees and have been qualified for publication in the conference proceedings [...]

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts