The Experts below are selected from a list of 8835 Experts worldwide ranked by ideXlab platform

David Jeffries - One of the best experts on this subject based on the ideXlab platform.

  • comparison of electronic data Capture edc with the standard data Capture Method for clinical trial data
    PLOS ONE, 2011
    Co-Authors: Brigitte Walther, Safayet Hossin, John Townend, Neil F Abernethy, David Parker, David Jeffries
    Abstract:

    Background Traditionally, clinical research studies rely on collecting data with case report forms, which are subsequently entered into a database to create electronic records. Although well established, this Method is time-consuming and error-prone. This study compares four electronic data Capture (EDC) Methods with the conventional approach with respect to duration of data Capture and accuracy. It was performed in a West African setting, where clinical trials involve data collection from urban, rural and often remote locations. Methodology/Principal Findings Three types of commonly available EDC tools were assessed in face-to-face interviews; netbook, PDA, and tablet PC. EDC performance during telephone interviews via mobile phone was evaluated as a fourth Method. The Graeco Latin square study design allowed comparison of all four Methods to standard paper-based recording followed by data double entry while controlling simultaneously for possible confounding factors such as interview order, interviewer and interviewee. Over a study period of three weeks the error rates decreased considerably for all EDC Methods. In the last week of the study the data accuracy for the netbook (5.1%, CI95%: 3.5–7.2%) and the tablet PC (5.2%, CI95%: 3.7–7.4%) was not significantly different from the accuracy of the conventional paper-based Method (3.6%, CI95%: 2.2–5.5%), but error rates for the PDA (7.9%, CI95%: 6.0–10.5%) and telephone (6.3%, CI95% 4.6–8.6%) remained significantly higher. While EDC-interviews take slightly longer, data become readily available after download, making EDC more time effective. Free text and date fields were associated with higher error rates than numerical, single select and skip fields. Conclusions EDC solutions have the potential to produce similar data accuracy compared to paper-based Methods. Given the considerable reduction in the time from data collection to database lock, EDC holds the promise to reduce research-associated costs. However, the successful implementation of EDC requires adjustment of work processes and reallocation of resources.

Yuji Hayashi - One of the best experts on this subject based on the ideXlab platform.

  • Omni-directional Polarization Image Sensor Based on an Omni-directional Camera and a Polarization Filter
    2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009
    Co-Authors: Yuukou Horita, Keiji Shibata, Kei Maeda, Yuji Hayashi
    Abstract:

    An effective Method for detecting road surface conditions is the use of polarization features. In this paper we describe this Method, for which we use two cameras for taking vertical and horizontal polarized images simultaneously. Generally, camera calibrations have to be processed, however in this paper we explain how we managed to avoid this processing, by introducing the omni-directional polarization image Capture Method, using an omni-directional camera and a polarization filter. This Method enables to take vertical and horizontal polarized images at the same time, without using the camera calibration.

  • Omni-directional polarization image Capture using omni-directional camera and polarization filter
    2008 8th International Conference on ITS Telecommunications, 2008
    Co-Authors: Yuukou Horita, Yuji Hayashi, Keiji Shibata, Kazunori Hayashi, Kouhei Morohashi
    Abstract:

    For detecting the road surface conditions, it is effective to use the polarization features. In this Method, we use two cameras for taking vertical and horizontal polarized images at a time. But, we have to process the camera calibration. In this paper, to remove this processing, we propose omni-directional polarization image Capture Method using omni-directional camera and polarization filter. This Method enables to take vertical and horizontal polarized images at a time, without using the camera calibration.

Yuukou Horita - One of the best experts on this subject based on the ideXlab platform.

  • Omni-directional Polarization Image Sensor Based on an Omni-directional Camera and a Polarization Filter
    2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009
    Co-Authors: Yuukou Horita, Keiji Shibata, Kei Maeda, Yuji Hayashi
    Abstract:

    An effective Method for detecting road surface conditions is the use of polarization features. In this paper we describe this Method, for which we use two cameras for taking vertical and horizontal polarized images simultaneously. Generally, camera calibrations have to be processed, however in this paper we explain how we managed to avoid this processing, by introducing the omni-directional polarization image Capture Method, using an omni-directional camera and a polarization filter. This Method enables to take vertical and horizontal polarized images at the same time, without using the camera calibration.

  • Omni-directional polarization image Capture using omni-directional camera and polarization filter
    2008 8th International Conference on ITS Telecommunications, 2008
    Co-Authors: Yuukou Horita, Yuji Hayashi, Keiji Shibata, Kazunori Hayashi, Kouhei Morohashi
    Abstract:

    For detecting the road surface conditions, it is effective to use the polarization features. In this Method, we use two cameras for taking vertical and horizontal polarized images at a time. But, we have to process the camera calibration. In this paper, to remove this processing, we propose omni-directional polarization image Capture Method using omni-directional camera and polarization filter. This Method enables to take vertical and horizontal polarized images at a time, without using the camera calibration.

Norio Tagawa - One of the best experts on this subject based on the ideXlab platform.

  • A study for assistant robotic system using motion Capture Method for adapting to human-robot interface
    2014 12th IEEE International Conference on Industrial Informatics (INDIN), 2014
    Co-Authors: Yihsin Ho, Eri Sato-shimokawara, Kazuyoshi Wada, Toru Yamaguchi, Norio Tagawa
    Abstract:

    In this paper, the authors present a user-centered concept for the operation experience of human-robot interface. For showing a concrete concept of our research, we design an assistant robot system. There are several stages for developing this system. Firstly, it is to obtain user's data. The authors obtained static data from Internet, and activity data from capturing humans' a motion data. The authors adapt accelerator sensor and image data, to Capture human motion. Secondly, the authors use association rule of data mining technique to find the hiding knowledge from user's data. The system can base on the found knowledge, which includes the relation about life patterns, psychological conditions and etc., to provide appropriate assistance. The system mainly includes the following steps: Capture user's motion data by accelerator sensor and stereo camera in indoor environment, analysis Captured motion data, found the hiding knowledge from user's data, recognize the environment through user's data, and based on these data and knowledge to provide service. In this paper, we design a human-centered assistant system for adapting to human-robot interface. The system not only uses static data, but also activity data using Capture Methods. We briefly describe the assistant robotic system's overall concept, and discuss the motion Captures Methods and knowledge finding. Simple experiment results will also be shown.

  • INDIN - A study for assistant robotic system using motion Capture Method for adapting to human-robot interface
    2014 12th IEEE International Conference on Industrial Informatics (INDIN), 2014
    Co-Authors: Yihsin Ho, Eri Sato-shimokawara, Kazuyoshi Wada, Toru Yamaguchi, Norio Tagawa
    Abstract:

    In this paper, the authors present a user-centered concept for the operation experience of human-robot interface. For showing a concrete concept of our research, we design an assistant robot system. There are several stages for developing this system. Firstly, it is to obtain user's data. The authors obtained static data from Internet, and activity data from capturing humans' a motion data. The authors adapt accelerator sensor and image data, to Capture human motion. Secondly, the authors use association rule of data mining technique to find the hiding knowledge from user's data. The system can base on the found knowledge, which includes the relation about life patterns, psychological conditions and etc., to provide appropriate assistance. The system mainly includes the following steps: Capture user's motion data by accelerator sensor and stereo camera in indoor environment, analysis Captured motion data, found the hiding knowledge from user's data, recognize the environment through user's data, and based on these data and knowledge to provide service. In this paper, we design a human-centered assistant system for adapting to human-robot interface. The system not only uses static data, but also activity data using Capture Methods. We briefly describe the assistant robotic system's overall concept, and discuss the motion Captures Methods and knowledge finding. Simple experiment results will also be shown.

Zhijun Li - One of the best experts on this subject based on the ideXlab platform.

  • Development of a hybrid motion Capture Method using MYO armband with application to teleoperation
    2016 IEEE International Conference on Mechatronics and Automation, 2016
    Co-Authors: Yanbin Xu, Chenguang Yang, Peidong Liang, Lijun Zhao, Zhijun Li
    Abstract:

    In this paper, we have developed a motion Capture Method based on data collected by MYO armband. The Method can be applied on any healthy operator wearing two MYO armbands on both upper and lower arms, respectively. The first MYO armband is worn near the centre of the operator's upper arm, the other is worn near the centre of the forearm. MYO armband has built-in eight bioelectrical sensors as well as a 9-axis IMU. The IMU sensors of the MYO are used to detect and reconstruct physical motion of shoulder and elbow joints, while the bioelectrical sensors are used to collect electromyography (EMG) signals associated with wrist motion. This hybrid Method enable us to fully Capture the motion of the 6-DOF (degree of freedom) of the arm. To test the proposed Method, hardware-in-loop simulations studies are performed, with both physiological and physical signals received and processed in MATLAB/Simulink via a low-power bluetooth interface. Results demonstrate the validness and effectiveness of the proposed Method.