2003-예방원년, 소아과 의사의 역할

Acting Under Secretary of Health requested that a working group be established to develop the Veterans Health Initiative (VHI). He envisioned this as a comprehensive program to recognize the connection between certain health effects and military service, to allow military history to be better documented, to prepare health care providers to better serve their veteran patients, and to establish a data base for further study. This was first discussed by the Acting Under Secretary in relation to the health of former prisoners of war. Development was really begun by the former Chief Academic Affairs Officer, Dr. David Stevens, with the Military Service History project. This involves a pocket card for medical residents detailing the important components of a military service history targeting the health risks associated with various periods of service and more generic issues of concern and a website containing references relevant to the issues. Educational modules in the Veterans Health Initiative VHA will assist health care providers in recognizing the connection between certain health effects and military service, prepare health care providers to better serve veteran patients, and will provide a data base for further study. This independent study is designed to provide primary care practitioners with an introduction to the pathologies that lead to sight loss, their functional implications, appropriate method of referrals, training programs, and special considerations for interactions with visually impaired individuals. After completing this independent study, participants would be able to: • Define legal blindness; • Describe the causes of sight loss; • Delineate the functional implications of vision loss • Delineate the psycho/social impact of vision loss on the veteran; • Outline the role of the Visual Impairment Services Team (VIST) in the treatment of legally blind veterans and the referral process; • Describe the special personal and environmental considerations needed for visually impaired patients; • Describe the special medical considerations needed for visually impaired patients; • Describe the primary care practitioner's role in assisting veterans in establishing well-grounded claims for disability related to the loss of vision; and • Describe compensation and pension benefits provided for veterans with eye disabilities. After completing this independent study, you should 1. be able to: state the definition of legal blindness; 2. be able to: associate eye diseases with their visual implications; 3. be able to: demonstrate insight into the functional and Psycho/Social implications of sight-loss; 4. know when referrals to VIST are indicated; 5. understand …

시력 손상과 시각 장애(Visual Impairment and Blindness)

2012 [AS13b, Wal12a]. 2013 [Alv13, Bar13, Cam13, Car13, CCS13, Cut13, Edi14, Swe13]. 2014 [CDS13, DN14, ND14, Rod14, Tym14a]. 2015 [DeL14a, DeL14b, She15b]. 2016 [AT16a, CCV16, CCV17, MR15, SDCT16a, SDCT16c, She16a]. 2017 [DG17, EC17, Fit18b, MJ18, TCSM17, TCM17]. 2018 [BG18b, DD17, Dec19b, Gol18, JI18, MKMP18, MMK18, PQH17, She18]. 2019 [Cut19, FI19, HPQ19, MP19a, MPRM19, MP19b, MS19, Rod18, She19a]. 2020 [DH19].

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A Complete Bibliography of Publications in SIGCSE Bulletin: 2000{2009

RiSH: A robot-integrated smart home for elderly care

Reconfigurable Computing. Accelerating Computation with Field-Programmable Gate Arrays by Maya B. Gokhale and Paul S. Graham Springer, 2005, 238 pp. ISBN-13 978-0387-26105-8, $87.20 Reconfigurable Computing Accelerating Computation with Field-Programmable Gate Arrays is an expository and easy to digest book. The authors are recognized leaders with many years of experience on the field of reconfigurable computing. The book is written so that non-specialists can understand the principles, techniques and algorithms. Each chapter has many excellent references for interested readers. It surveys methods, algorithms, programming languages and applications targeted to reconfigurable computing. Automatic generation of parallel code from a sequential program on conventional micro-processor architectures remains an open problem. Nevertheless, a wide range of computationally intensive applications have benefited from many tools developed to tackle such a problem. For RC, it is even a much harder problem (perhaps 10x and up) and intense research is being devoted to make RC a common-place practical tool. The aim of the authors is threefold. First, guide the readers to know current issues on HLL for RC. Second, help the readers understand the intricate process of algorithmic-to-hardware compilation. And third, show that, even though this process is painful, if the application is suitable for RC the gains in performance are huge. The book is divided into two parts. The first part contains four chapters about reconfigurable computing and languages. Chapter 1 presents an introduction of RC, contrasting conventional fixed instruction microprocessors with RC architectures. This chapter also contains comprehensive reference material for further reading. Chapter 2 introduces reconfigurable logic devices by explaining the basic architecture and configuration of FPGAs. Chapter 3 deals with RC systems by discussing how parallel processing is achieved on reconfigurable computers and also gives a survey of RC systems today. Then, in chapter 4, languages, compilation, debugging and their related manual vs. automatic issues are discussed. The second part of the book comprises five chapters about applications of RC. Chapter 5 and 6 discuss digital signal and image processing applications. Chapter 7 covers the application of RC to secure network communications. The aim of Chapter 8 is to discuss some important bioinformatics applications for which RC is a good candidate, their algorithmic problems and hardware implementations. Finally, Chapter 9 covers two applications of reconfigurable supercomputers. The first one is a simulation of radiative heat transfer and the second one models large urban road traffic. This book is neither a technical nor a text book, but in the opinion of this reviewer, it is an excellent state-of-the-art review of RC and would be a worthwhile acquisition by anyone seriously considering speeding up a specific application. On the downside, it is somewhat disappointing that the book does not contain more information about HLL tools that could be used to help close the gap between traditional HPC community and the raw computing power of RC. Edusmildo Orozco, Department of Computer Science, University Of Puerto Rico at Rio Piedras.

Reconfigurable Computing. Accelerating Computation with Field-Programmable Gate Arrays

Gait phase detection is essential for clinicians to examine the gait cycles of human test subjects. In this paper, a method to detect normal and abnormal gait phases using a single inertial measurement unit attached to the shank is proposed. The local maxima, minima, and zero crossings of the shank’s angular velocity and acceleration are used to detect heel strike, toe strike, and toe off. The proposed method may provide human-understandable insights into the shank’s angular velocity and acceleration to assist clinicians to evaluate the patients’ gaits. The proposed method is compared with an existing method that uses force sensitive resistors placed under the foot. The mean absolute sample difference between the proposed and existing methods is low at around 2 sample difference, equivalent to 20 ms difference.

Gait Phase Detection for Normal and Abnormal Gaits Using IMU

Accurate step counting is important in pedometer based indoor localization. Existing step detection techniques are not sufficiently accurate, especially at low walking speeds that are commonly observed when navigating unfamiliar environments. This is more critical when vision impaired indoor navigation is considered due to the fact that they have relatively low walking speeds. Almost all existing pedometer techniques use accelerometer data to identify steps, which is not very accurate at low walking speeds. This paper describes a gyroscope based pedometer algorithm implemented in a smartphone. The smartphone is placed in the pocket of the trouser, which is a usual carrying position of the mobile phone. The gyroscope sensor data is used for the identification of steps. The algorithm was designed to demand minimal computational resources so that it can be easily implemented in an embedded platform. Raw data from the sensor are filtered using a 6th order Butterworth filter for noise reduction. This is then sent though a zero crossing detector which identifies the steps. A minimum delay between two consecutive zero crossings was used to avoid fluctuations being counted and peak detection was used to validate steps. The algorithm has a calibration mode, in which the absolute minimum swing of data is learnt to set the threshold. This approach demonstrated accuracies above 96% even at slow walking speeds on flat land, above 95% when walking up/down hills and above 90% when going up/down stairs. This has supported the concept that the gyroscope can be used efficiently in step identification for indoor positioning and navigation systems.

A Gyroscope Based Accurate Pedometer Algorithm

Human gait analysis is a major topic in pedestrian navigation and geriatric care. Identifying gait phases is important in using human gait for pedestrian navigation and tracking. Most of existing gait phase identification techniques use multiple sensor modules attached to each section of the lower body. This paper discusses the feasibility of recognizing gait phases using a single inertial measurement unit (IMU) placed in a trouser pocket of the subject. The movement of the thigh is computed by fusing accelerometer and the gyroscopic data gathered from the of the IMU. Experimental results indicated that most of the major gait phases such as Initial Contact, Load Response, Mid Stance, Terminal Stance, Pre-Swing and Swing, can be identified by the movement of one thigh tracked by an IMU. It was also noted that the movement of the offside leg can also be estimated from the fused IMU data. This paper presents a method to recognize all major phases of human stride cycle during walking from movement of one thigh.

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Human gait phase recognition based on thigh movement computed using IMUs

Gait analysis is relevant to a broad range of clinical applications in areas of orthopedics, neurosurgery, rehabilitation and the sports medicine. There are various methods available for capturing and analyzing the gait cycle. Most of gait analysis methods are computationally expensive and difficult to implement outside the laboratory environment. Inertial measurement units, IMUs are considered a promising alternative for the future of gait analysis. This study reports the results of a systematic validation procedure to validate the foot pitch angle measurement captured by an IMU against Vicon Optical Motion Capture System, considered the standard method of gait analysis. It represents the first phase of a research project which aims to objectively evaluate the ankle function and gait patterns of patients with dorsiflexion weakness (commonly called a "drop foot") due to a L5 lumbar radiculopathy pre- and post-lumbar decompression surgery. The foot pitch angle of 381 gait cycles from 19 subjects walking trails on a flat surface have been recorded throughout the course of this study. Comparison of results indicates a mean correlation of 99.542% with a standard deviation of 0.834%. The maximum root mean square error of the foot pitch angle measured by the IMU compared with the Vicon Optical Motion Capture System was 3.738° and the maximum error in the same walking trail between two measurements was 9.927°. These results indicate the level of correlation between the two systems.

Iain Murray

Papers

Gait Phase Detection for Normal and Abnormal Gaits Using IMU

A Gyroscope Based Accurate Pedometer Algorithm

Human gait phase recognition based on thigh movement computed using IMUs

Validation of foot pitch angle estimation using inertial measurement unit against marker-based optical 3D motion capture system

Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms.