The Experts below are selected from a list of 25101 Experts worldwide ranked by ideXlab platform

Neeranut Ratchatanantakit - One of the best experts on this subject based on the ideXlab platform.

  • HCI (2) - Consistency Study of 3D Magnetic Vectors in an Office Environment for IMU-based Hand Tracking Input Development.
    Human-Computer Interaction. Recognition and Interaction Technologies, 2019
    Co-Authors: Neeranut Ratchatanantakit, Nonnarit O-larnnithipong, Armando Barreto, Sudarat Tangnimitchok
    Abstract:

    This paper reports our study of the distortion of the Magnetic North Vector in an office environment, which affects the use of commercial-grade Inertial Measurement Units (IMUs) for orientation tracking in a 3D hand motion tracking interface. The study includes data collected for 30 days to analyze the effect that may occur in terms of error and consistency. The Magnetic North Vector is one of the data sets used in orientation correction algorithms to reduce the bias offset error in IMUs. A non-ferroMagnetic frame was made to define 125 points inside a cubic space in an office. Data was recorded for 30 days and analyzed to define the variation of errors. Experiment results show that the metal from a desk creates a Magnetic distortion. In some areas the deviation of the Magnetic North Vector is severe and the amount of variation error is not uniform.

  • HCI (2) - Evaluation of Orientation Correction Algorithms in Real-Time Hand Motion Tracking for Computer Interaction
    Human-Computer Interaction. Recognition and Interaction Technologies, 2019
    Co-Authors: Nonnarit O-larnnithipong, Neeranut Ratchatanantakit, Armando Barreto, Sudarat Tangnimitchok
    Abstract:

    This paper outlines the evaluation of orientation correction algorithms implemented in a hand motion tracking system that utilizes an inertial measurement unit (IMU) and infrared cameras. Thirty human subjects participated in an experiment to validate the performance of the hand motion tracking system. The statistical analysis shows that the error of position tracking is, on average, 1.7 cm in the x-axis, 1.0 cm in the y-axis, and 3.5 cm in the z-axis. The Kruskal-Wallis tests show that the orientation correction algorithm using gravity vector and Magnetic North vector can significantly reduce the errors in orientation tracking in comparison to fixed offset compensation. Statistical analyses show that the orientation correction algorithm using gravity vector and Magnetic North vector and the on-board Kalman-based orientation filtering produced orientation errors that were not significantly different in the Euler angles, Phi, Theta and Psi, with the p-values of 0.632, 0.262 and 0.728, respectively.

  • Evaluation of Orientation Correction Algorithms in Real-Time Hand Motion Tracking for Computer Interaction
    Human-Computer Interaction. Recognition and Interaction Technologies, 2019
    Co-Authors: Nonnarit O-larnnithipong, Neeranut Ratchatanantakit, Armando Barreto, Sudarat Tangnimitchok
    Abstract:

    This paper outlines the evaluation of orientation correction algorithms implemented in a hand motion tracking system that utilizes an inertial measurement unit (IMU) and infrared cameras. Thirty human subjects participated in an experiment to validate the performance of the hand motion tracking system. The statistical analysis shows that the error of position tracking is, on average, 1.7 cm in the x-axis, 1.0 cm in the y-axis, and 3.5 cm in the z-axis. The Kruskal-Wallis tests show that the orientation correction algorithm using gravity vector and Magnetic North vector can significantly reduce the errors in orientation tracking in comparison to fixed offset compensation. Statistical analyses show that the orientation correction algorithm using gravity vector and Magnetic North vector and the on-board Kalman-based orientation filtering produced orientation errors that were not significantly different in the Euler angles, Phi, Theta and Psi, with the p-values of 0.632, 0.262 and 0.728, respectively.

  • HCI (7) - Real-Time Implementation of Orientation Correction Algorithm for 3D Hand Motion Tracking Interface
    Universal Access in Human-Computer Interaction. Methods Technologies and Users, 2018
    Co-Authors: Nonnarit O-larnnithipong, Neeranut Ratchatanantakit, Sudarat Tangnimitchok, Armando Barreto, Francisco R. Ortega
    Abstract:

    This paper outlines the real-time implementation of an orientation correction algorithm using the gravity vector and the Magnetic North vector for a miniature, commercial-grade Inertial Measurement Unit to improve orientation tracking in 3D hand motion tracking interface. The algorithm uses the sensor fusion approach to determine the correct orientation of the human hand motion in 3D environment. The bias offset error is the IMU’s systematic error that can cause a problem in orientation tracking called drift. The algorithm is able to determine the bias offset error and update the gyroscope reading to obtain unbiased angular velocity. Furthermore, the algorithm will compare the initial estimated orientation result by using other referencing sources which are the gravity vector measured from the accelerometer and the Magnetic North vector measured from the magnetometer, resulting in the improvement of the estimated orientation. The orientation correction algorithm is implemented in real-time within Unity along with position tracking, through a system of infrared cameras. To validate the performance of the real-time implementation, the orientation estimated from the algorithm and the position obtained from the infrared cameras are applied to a 3D hand model. An experiment requiring the acquisition of cubic targets within a 3D environment using the 3D hand motion tracking interface was performed 30 times. Experimental results show that the algorithm can be implemented in real-time and can eliminate the drift in orientation tracking.

  • HCI (3) - Orientation Correction for a 3D Hand Motion Tracking Interface Using Inertial Measurement Units
    Lecture Notes in Computer Science, 2018
    Co-Authors: Nonnarit O-larnnithipong, Sudarat Tangnimitchok, Armando Barreto, Neeranut Ratchatanantakit
    Abstract:

    This paper outlines the use of an orientation correction algorithm for a miniature commercial-grade Inertial Measurement Unit to improve orientation tracking of human hand motion and also to improve 3D User Interfaces experience to become more realistic. The algorithm uses the combination of gyroscope, accelerometer and magnetometer measurements to eliminate the drift in orientation measurement which is caused by the accumulation of the bias offset error in the gyroscope readings. The algorithm consists of three parts, which are: (1) bias offset estimation, (2) quaternion correction using gravity vector and Magnetic North vector, and (3) quaternion interpolation. The bias offset estimation is performed during periods when the sensor is estimated to be static, when the gyroscope reading would provide only the bias offset error for prediction. The quaternion was calculated based on unbiased angular velocity and then used to rotate the gravity vector and Magnetic North vector in the Earth’s frame resulting in the calculated gravity vector and Magnetic North vector in the sensor’s frame. The angular errors between calculated and measured gravity vector and the angle between calculated and measured Magnetic North vector are used to calculate the correction quaternion that must be applied to the previous quaternion result. The result of the orientation estimation using this algorithm can be used to track the orientation of human hand motion with less drift and improved orientation accuracy than achieved with the on-board Kalman-based orientation filtering.

Ulf Ottosson - One of the best experts on this subject based on the ideXlab platform.

  • Orientation of snow buntings (Plectrophenax nivalis) close to the Magnetic North pole
    The Journal of Experimental Biology, 1998
    Co-Authors: Roland Sandberg, Johan Bäckman, Ulf Ottosson
    Abstract:

    Orientation experiments were performed with first-year snow buntings (Plectrophenax nivalis) during their autumn migration in a natural near-vertical geoMagnetic field approximately 400 km away from the Magnetic North pole. Migratory orientation of snow buntings was recorded using two different techniques: orientation cage tests and freeflight release experiments. Experiments were performed under clear skies, as well as under natural and simulated complete overcast. Several experimental manipulations were performed including an artificial shift of the E-vector direction of polarized light, depolarization of incoming light and a 4 h slow clock-shift experiment. The amount of stored fat proved to be decisive for the directional selections of the buntings. Fat individuals generally chose southerly mean directions, whereas lean birds selected Northerly headings. These directional selections seemed to be independent of experimental manipulations of the buntings’ access to visual cues even in the local near-vertical Magnetic field. Under clear skies, the buntings failed to respond to either a deflection of the E-vector direction of polarized light or an experimental depolarization of incoming skylight. When tested under natural as well as simulated overcast, the buntings were still able to select a meaningful mean direction according to their fat status. Similarly, the free-flight release test under complete overcast resulted in a well-defined southsoutheast direction, possibly influenced by the prevailing light Northwest wind. Clock-shift experiments did not yield a conclusive result, but the failure of these birds to take off during the subsequent free-flight release test may indicate some unspecified confusion effect of the treatment. (Less)

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

  • Orientation of snow buntings (Plectrophenax nivalis) close to the Magnetic North pole
    The Journal of experimental biology, 1998
    Co-Authors: Sandberg, Backman, Ottosson
    Abstract:

    Orientation experiments were performed with first-year snow buntings (Plectrophenax nivalis) during their autumn migration in a natural near-vertical geoMagnetic field approximately 400 km away from the Magnetic North pole. Migratory orientation of snow buntings was recorded using two different techniques: orientation cage tests and free-flight release experiments. Experiments were performed under clear skies, as well as under natural and simulated complete overcast. Several experimental manipulations were performed including an artificial shift of the E-vector direction of polarized light, depolarization of incoming light and a 4 h slow clock-shift experiment. The amount of stored fat proved to be decisive for the directional selections of the buntings. Fat individuals generally chose southerly mean directions, whereas lean birds selected Northerly headings. These directional selections seemed to be independent of experimental manipulations of the buntings' access to visual cues even in the local near-vertical Magnetic field. Under clear skies, the buntings failed to respond to either a deflection of the E-vector direction of polarized light or an experimental depolarization of incoming skylight. When tested under natural as well as simulated overcast, the buntings were still able to select a meaningful mean direction according to their fat status. Similarly, the free-flight release test under complete overcast resulted in a well-defined southsoutheast direction, possibly influenced by the prevailing light Northwest wind. Clock-shift experiments did not yield a conclusive result, but the failure of these birds to take off during the subsequent free-flight release test may indicate some unspecified confusion effect of the treatment.

Nonnarit O-larnnithipong - One of the best experts on this subject based on the ideXlab platform.

  • HCI (2) - Consistency Study of 3D Magnetic Vectors in an Office Environment for IMU-based Hand Tracking Input Development.
    Human-Computer Interaction. Recognition and Interaction Technologies, 2019
    Co-Authors: Neeranut Ratchatanantakit, Nonnarit O-larnnithipong, Armando Barreto, Sudarat Tangnimitchok
    Abstract:

    This paper reports our study of the distortion of the Magnetic North Vector in an office environment, which affects the use of commercial-grade Inertial Measurement Units (IMUs) for orientation tracking in a 3D hand motion tracking interface. The study includes data collected for 30 days to analyze the effect that may occur in terms of error and consistency. The Magnetic North Vector is one of the data sets used in orientation correction algorithms to reduce the bias offset error in IMUs. A non-ferroMagnetic frame was made to define 125 points inside a cubic space in an office. Data was recorded for 30 days and analyzed to define the variation of errors. Experiment results show that the metal from a desk creates a Magnetic distortion. In some areas the deviation of the Magnetic North Vector is severe and the amount of variation error is not uniform.

  • HCI (2) - Evaluation of Orientation Correction Algorithms in Real-Time Hand Motion Tracking for Computer Interaction
    Human-Computer Interaction. Recognition and Interaction Technologies, 2019
    Co-Authors: Nonnarit O-larnnithipong, Neeranut Ratchatanantakit, Armando Barreto, Sudarat Tangnimitchok
    Abstract:

    This paper outlines the evaluation of orientation correction algorithms implemented in a hand motion tracking system that utilizes an inertial measurement unit (IMU) and infrared cameras. Thirty human subjects participated in an experiment to validate the performance of the hand motion tracking system. The statistical analysis shows that the error of position tracking is, on average, 1.7 cm in the x-axis, 1.0 cm in the y-axis, and 3.5 cm in the z-axis. The Kruskal-Wallis tests show that the orientation correction algorithm using gravity vector and Magnetic North vector can significantly reduce the errors in orientation tracking in comparison to fixed offset compensation. Statistical analyses show that the orientation correction algorithm using gravity vector and Magnetic North vector and the on-board Kalman-based orientation filtering produced orientation errors that were not significantly different in the Euler angles, Phi, Theta and Psi, with the p-values of 0.632, 0.262 and 0.728, respectively.

  • Evaluation of Orientation Correction Algorithms in Real-Time Hand Motion Tracking for Computer Interaction
    Human-Computer Interaction. Recognition and Interaction Technologies, 2019
    Co-Authors: Nonnarit O-larnnithipong, Neeranut Ratchatanantakit, Armando Barreto, Sudarat Tangnimitchok
    Abstract:

    This paper outlines the evaluation of orientation correction algorithms implemented in a hand motion tracking system that utilizes an inertial measurement unit (IMU) and infrared cameras. Thirty human subjects participated in an experiment to validate the performance of the hand motion tracking system. The statistical analysis shows that the error of position tracking is, on average, 1.7 cm in the x-axis, 1.0 cm in the y-axis, and 3.5 cm in the z-axis. The Kruskal-Wallis tests show that the orientation correction algorithm using gravity vector and Magnetic North vector can significantly reduce the errors in orientation tracking in comparison to fixed offset compensation. Statistical analyses show that the orientation correction algorithm using gravity vector and Magnetic North vector and the on-board Kalman-based orientation filtering produced orientation errors that were not significantly different in the Euler angles, Phi, Theta and Psi, with the p-values of 0.632, 0.262 and 0.728, respectively.

  • HCI (7) - Real-Time Implementation of Orientation Correction Algorithm for 3D Hand Motion Tracking Interface
    Universal Access in Human-Computer Interaction. Methods Technologies and Users, 2018
    Co-Authors: Nonnarit O-larnnithipong, Neeranut Ratchatanantakit, Sudarat Tangnimitchok, Armando Barreto, Francisco R. Ortega
    Abstract:

    This paper outlines the real-time implementation of an orientation correction algorithm using the gravity vector and the Magnetic North vector for a miniature, commercial-grade Inertial Measurement Unit to improve orientation tracking in 3D hand motion tracking interface. The algorithm uses the sensor fusion approach to determine the correct orientation of the human hand motion in 3D environment. The bias offset error is the IMU’s systematic error that can cause a problem in orientation tracking called drift. The algorithm is able to determine the bias offset error and update the gyroscope reading to obtain unbiased angular velocity. Furthermore, the algorithm will compare the initial estimated orientation result by using other referencing sources which are the gravity vector measured from the accelerometer and the Magnetic North vector measured from the magnetometer, resulting in the improvement of the estimated orientation. The orientation correction algorithm is implemented in real-time within Unity along with position tracking, through a system of infrared cameras. To validate the performance of the real-time implementation, the orientation estimated from the algorithm and the position obtained from the infrared cameras are applied to a 3D hand model. An experiment requiring the acquisition of cubic targets within a 3D environment using the 3D hand motion tracking interface was performed 30 times. Experimental results show that the algorithm can be implemented in real-time and can eliminate the drift in orientation tracking.

  • HCI (3) - Orientation Correction for a 3D Hand Motion Tracking Interface Using Inertial Measurement Units
    Lecture Notes in Computer Science, 2018
    Co-Authors: Nonnarit O-larnnithipong, Sudarat Tangnimitchok, Armando Barreto, Neeranut Ratchatanantakit
    Abstract:

    This paper outlines the use of an orientation correction algorithm for a miniature commercial-grade Inertial Measurement Unit to improve orientation tracking of human hand motion and also to improve 3D User Interfaces experience to become more realistic. The algorithm uses the combination of gyroscope, accelerometer and magnetometer measurements to eliminate the drift in orientation measurement which is caused by the accumulation of the bias offset error in the gyroscope readings. The algorithm consists of three parts, which are: (1) bias offset estimation, (2) quaternion correction using gravity vector and Magnetic North vector, and (3) quaternion interpolation. The bias offset estimation is performed during periods when the sensor is estimated to be static, when the gyroscope reading would provide only the bias offset error for prediction. The quaternion was calculated based on unbiased angular velocity and then used to rotate the gravity vector and Magnetic North vector in the Earth’s frame resulting in the calculated gravity vector and Magnetic North vector in the sensor’s frame. The angular errors between calculated and measured gravity vector and the angle between calculated and measured Magnetic North vector are used to calculate the correction quaternion that must be applied to the previous quaternion result. The result of the orientation estimation using this algorithm can be used to track the orientation of human hand motion with less drift and improved orientation accuracy than achieved with the on-board Kalman-based orientation filtering.

Sudarat Tangnimitchok - One of the best experts on this subject based on the ideXlab platform.

  • HCI (2) - Consistency Study of 3D Magnetic Vectors in an Office Environment for IMU-based Hand Tracking Input Development.
    Human-Computer Interaction. Recognition and Interaction Technologies, 2019
    Co-Authors: Neeranut Ratchatanantakit, Nonnarit O-larnnithipong, Armando Barreto, Sudarat Tangnimitchok
    Abstract:

    This paper reports our study of the distortion of the Magnetic North Vector in an office environment, which affects the use of commercial-grade Inertial Measurement Units (IMUs) for orientation tracking in a 3D hand motion tracking interface. The study includes data collected for 30 days to analyze the effect that may occur in terms of error and consistency. The Magnetic North Vector is one of the data sets used in orientation correction algorithms to reduce the bias offset error in IMUs. A non-ferroMagnetic frame was made to define 125 points inside a cubic space in an office. Data was recorded for 30 days and analyzed to define the variation of errors. Experiment results show that the metal from a desk creates a Magnetic distortion. In some areas the deviation of the Magnetic North Vector is severe and the amount of variation error is not uniform.

  • HCI (2) - Evaluation of Orientation Correction Algorithms in Real-Time Hand Motion Tracking for Computer Interaction
    Human-Computer Interaction. Recognition and Interaction Technologies, 2019
    Co-Authors: Nonnarit O-larnnithipong, Neeranut Ratchatanantakit, Armando Barreto, Sudarat Tangnimitchok
    Abstract:

    This paper outlines the evaluation of orientation correction algorithms implemented in a hand motion tracking system that utilizes an inertial measurement unit (IMU) and infrared cameras. Thirty human subjects participated in an experiment to validate the performance of the hand motion tracking system. The statistical analysis shows that the error of position tracking is, on average, 1.7 cm in the x-axis, 1.0 cm in the y-axis, and 3.5 cm in the z-axis. The Kruskal-Wallis tests show that the orientation correction algorithm using gravity vector and Magnetic North vector can significantly reduce the errors in orientation tracking in comparison to fixed offset compensation. Statistical analyses show that the orientation correction algorithm using gravity vector and Magnetic North vector and the on-board Kalman-based orientation filtering produced orientation errors that were not significantly different in the Euler angles, Phi, Theta and Psi, with the p-values of 0.632, 0.262 and 0.728, respectively.

  • Evaluation of Orientation Correction Algorithms in Real-Time Hand Motion Tracking for Computer Interaction
    Human-Computer Interaction. Recognition and Interaction Technologies, 2019
    Co-Authors: Nonnarit O-larnnithipong, Neeranut Ratchatanantakit, Armando Barreto, Sudarat Tangnimitchok
    Abstract:

    This paper outlines the evaluation of orientation correction algorithms implemented in a hand motion tracking system that utilizes an inertial measurement unit (IMU) and infrared cameras. Thirty human subjects participated in an experiment to validate the performance of the hand motion tracking system. The statistical analysis shows that the error of position tracking is, on average, 1.7 cm in the x-axis, 1.0 cm in the y-axis, and 3.5 cm in the z-axis. The Kruskal-Wallis tests show that the orientation correction algorithm using gravity vector and Magnetic North vector can significantly reduce the errors in orientation tracking in comparison to fixed offset compensation. Statistical analyses show that the orientation correction algorithm using gravity vector and Magnetic North vector and the on-board Kalman-based orientation filtering produced orientation errors that were not significantly different in the Euler angles, Phi, Theta and Psi, with the p-values of 0.632, 0.262 and 0.728, respectively.

  • HCI (7) - Real-Time Implementation of Orientation Correction Algorithm for 3D Hand Motion Tracking Interface
    Universal Access in Human-Computer Interaction. Methods Technologies and Users, 2018
    Co-Authors: Nonnarit O-larnnithipong, Neeranut Ratchatanantakit, Sudarat Tangnimitchok, Armando Barreto, Francisco R. Ortega
    Abstract:

    This paper outlines the real-time implementation of an orientation correction algorithm using the gravity vector and the Magnetic North vector for a miniature, commercial-grade Inertial Measurement Unit to improve orientation tracking in 3D hand motion tracking interface. The algorithm uses the sensor fusion approach to determine the correct orientation of the human hand motion in 3D environment. The bias offset error is the IMU’s systematic error that can cause a problem in orientation tracking called drift. The algorithm is able to determine the bias offset error and update the gyroscope reading to obtain unbiased angular velocity. Furthermore, the algorithm will compare the initial estimated orientation result by using other referencing sources which are the gravity vector measured from the accelerometer and the Magnetic North vector measured from the magnetometer, resulting in the improvement of the estimated orientation. The orientation correction algorithm is implemented in real-time within Unity along with position tracking, through a system of infrared cameras. To validate the performance of the real-time implementation, the orientation estimated from the algorithm and the position obtained from the infrared cameras are applied to a 3D hand model. An experiment requiring the acquisition of cubic targets within a 3D environment using the 3D hand motion tracking interface was performed 30 times. Experimental results show that the algorithm can be implemented in real-time and can eliminate the drift in orientation tracking.

  • HCI (3) - Orientation Correction for a 3D Hand Motion Tracking Interface Using Inertial Measurement Units
    Lecture Notes in Computer Science, 2018
    Co-Authors: Nonnarit O-larnnithipong, Sudarat Tangnimitchok, Armando Barreto, Neeranut Ratchatanantakit
    Abstract:

    This paper outlines the use of an orientation correction algorithm for a miniature commercial-grade Inertial Measurement Unit to improve orientation tracking of human hand motion and also to improve 3D User Interfaces experience to become more realistic. The algorithm uses the combination of gyroscope, accelerometer and magnetometer measurements to eliminate the drift in orientation measurement which is caused by the accumulation of the bias offset error in the gyroscope readings. The algorithm consists of three parts, which are: (1) bias offset estimation, (2) quaternion correction using gravity vector and Magnetic North vector, and (3) quaternion interpolation. The bias offset estimation is performed during periods when the sensor is estimated to be static, when the gyroscope reading would provide only the bias offset error for prediction. The quaternion was calculated based on unbiased angular velocity and then used to rotate the gravity vector and Magnetic North vector in the Earth’s frame resulting in the calculated gravity vector and Magnetic North vector in the sensor’s frame. The angular errors between calculated and measured gravity vector and the angle between calculated and measured Magnetic North vector are used to calculate the correction quaternion that must be applied to the previous quaternion result. The result of the orientation estimation using this algorithm can be used to track the orientation of human hand motion with less drift and improved orientation accuracy than achieved with the on-board Kalman-based orientation filtering.