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Accelerometer Sensor

The Experts below are selected from a list of 5067 Experts worldwide ranked by ideXlab platform

Zeljko Zilic – 1st expert on this subject based on the ideXlab platform

  • MobiHealth – Tidal volume variability and respiration rate estimation using a wearable Accelerometer Sensor
    , 2014
    Co-Authors: Atena Roshan Fekr, Katarzyna Radecka, Zeljko Zilic

    Abstract:

    The measurement of respiration rate and tidal volume variability are critical to the diagnosis and monitoring of a wide range of breath disorders as well as being useful broader parameters of a patient’s condition. This paper presents a portable real-time platform designed to support a computationally efficient human respiratory tracking system for medical applications. The proposed system is designed particularly for patients with breathing problems (e.g. respiratory complications after surgery) or sleep disorders. We introduce the use of Accelerometer Sensor to detect changes in the anterior-posterior diameter of the chest; whereas these changes provide an accurate measurement of respiration rate as well as tidal volume variability. The complete system was comprised of wearable calibrated Accelerometer Sensor, Bluetooth Low Energy (BLE) and cloud database. The experiments are conducted with 8 subjects and the overall error in respiration rate calculation is obtained 0.2% considering SPR-BTA spirometer as the reference. We also present a method for Tidal Volume variability (TVvar) estimation while validated using Pearson correlation. The mean value of the correlation coefficient between TVvar derived from the Accelerometer and spirometer for all subjects and three breath patterns is 0.87 which shows a high correspondence of two signals. Furthermore, the results indicate that the Accelerometer driven TVvar achieves the average MSE 1.6E-03±3.69E-03 compared to the reference.

  • Tidal volume variability and respiration rate estimation using a wearable Accelerometer Sensor
    2014 4th International Conference on Wireless Mobile Communication and Healthcare – Transforming Healthcare Through Innovations in Mobile and Wireless, 2014
    Co-Authors: Atena Roshan Fekr, Katarzyna Radecka, Zeljko Zilic

    Abstract:

    The measurement of respiration rate and tidal volume variability are critical to the diagnosis and monitoring of a wide range of breath disorders as well as being useful broader parameters of a patient’s condition. This paper presents a portable real-time platform designed to support a computationally efficient human respiratory tracking system for medical applications. The proposed system is designed particularly for patients with breathing problems (e.g. respiratory complications after surgery) or sleep disorders. We introduce the use of Accelerometer Sensor to detect changes in the anterior-posterior diameter of the chest; whereas these changes provide an accurate measurement of respiration rate as well as tidal volume variability. The complete system was comprised of wearable calibrated Accelerometer Sensor, Bluetooth Low Energy (BLE) and cloud database. The experiments are conducted with 8 subjects and the overall error in respiration rate calculation is obtained 0.2% considering SPR-BTA spirometer as the reference. We also present a method for Tidal Volume variability (TVvar) estimation while validated using Pearson correlation. The mean value of the correlation coefficient between TVvar derived from the Accelerometer and spirometer for all subjects and three breath patterns is 0.87 which shows a high correspondence of two signals. Furthermore, the results indicate that the Accelerometer driven TVvar achieves the average MSE 1.6E-03±3.69E-03 compared to the reference.

Paul S. Fisher – 2nd expert on this subject based on the ideXlab platform

  • SMC – Motion recognition with smart phone embedded 3-axis Accelerometer Sensor
    2012 IEEE International Conference on Systems Man and Cybernetics (SMC), 2012
    Co-Authors: Jinsuk Baek, Paul S. Fisher

    Abstract:

    As the technology surrounding smart phone devices has changed over the past few years, we now find a device containing a collection of Sensors. Indeed, one can say that the development of smart phones has been one of the most important advances in science and technology. We will show an additional usage for the smart phone: utilizing it for a generic, hardware, gaming controller. We will show how a motion recognition mechanism can be used for determining rate of change and position of the phone as it moves in 3D-space using the embedded 3-axes Accelerometer Sensor. Upon sensing a user’s motions with the smart phone, the corresponding Accelerometer values are transmitted to the gaming console through the Wi-Fi communication. Motion recognition is then performed at the gaming console using a pattern matching mechanism. The proposed mechanism is applied to the game of tennis to recognize three primary ground stroke motions: the forehand stroke, backhand stroke, and service. With individual calibration for these three motions, we show how accurately the system can recognize the motions, and derive ball-hit likelihood. These types of results, when fully realized, can provide a much richer and simpler experience for the user.

  • Motion recognition with smart phone embedded 3-axis Accelerometer Sensor
    2012 IEEE International Conference on Systems Man and Cybernetics (SMC), 2012
    Co-Authors: Jinsuk Baek, Paul S. Fisher

    Abstract:

    As the technology surrounding smart phone devices has changed over the past few years, we now find a device containing a collection of Sensors. Indeed, one can say that the development of smart phones has been one of the most important advances in science and technology. We will show an additional usage for the smart phone: utilizing it for a generic, hardware, gaming controller. We will show how a motion recognition mechanism can be used for determining rate of change and position of the phone as it moves in 3D-space using the embedded 3-axes Accelerometer Sensor. Upon sensing a user’s motions with the smart phone, the corresponding Accelerometer values are transmitted to the gaming console through the Wi-Fi communication. Motion recognition is then performed at the gaming console using a pattern matching mechanism. The proposed mechanism is applied to the game of tennis to recognize three primary ground stroke motions: the forehand stroke, backhand stroke, and service. With individual calibration for these three motions, we show how accurately the system can recognize the motions, and derive ball-hit likelihood. These types of results, when fully realized, can provide a much richer and simpler experience for the user.

Atena Roshan Fekr – 3rd expert on this subject based on the ideXlab platform

  • MobiHealth – Tidal volume variability and respiration rate estimation using a wearable Accelerometer Sensor
    , 2014
    Co-Authors: Atena Roshan Fekr, Katarzyna Radecka, Zeljko Zilic

    Abstract:

    The measurement of respiration rate and tidal volume variability are critical to the diagnosis and monitoring of a wide range of breath disorders as well as being useful broader parameters of a patient’s condition. This paper presents a portable real-time platform designed to support a computationally efficient human respiratory tracking system for medical applications. The proposed system is designed particularly for patients with breathing problems (e.g. respiratory complications after surgery) or sleep disorders. We introduce the use of Accelerometer Sensor to detect changes in the anterior-posterior diameter of the chest; whereas these changes provide an accurate measurement of respiration rate as well as tidal volume variability. The complete system was comprised of wearable calibrated Accelerometer Sensor, Bluetooth Low Energy (BLE) and cloud database. The experiments are conducted with 8 subjects and the overall error in respiration rate calculation is obtained 0.2% considering SPR-BTA spirometer as the reference. We also present a method for Tidal Volume variability (TVvar) estimation while validated using Pearson correlation. The mean value of the correlation coefficient between TVvar derived from the Accelerometer and spirometer for all subjects and three breath patterns is 0.87 which shows a high correspondence of two signals. Furthermore, the results indicate that the Accelerometer driven TVvar achieves the average MSE 1.6E-03±3.69E-03 compared to the reference.

  • Tidal volume variability and respiration rate estimation using a wearable Accelerometer Sensor
    2014 4th International Conference on Wireless Mobile Communication and Healthcare – Transforming Healthcare Through Innovations in Mobile and Wireless, 2014
    Co-Authors: Atena Roshan Fekr, Katarzyna Radecka, Zeljko Zilic

    Abstract:

    The measurement of respiration rate and tidal volume variability are critical to the diagnosis and monitoring of a wide range of breath disorders as well as being useful broader parameters of a patient’s condition. This paper presents a portable real-time platform designed to support a computationally efficient human respiratory tracking system for medical applications. The proposed system is designed particularly for patients with breathing problems (e.g. respiratory complications after surgery) or sleep disorders. We introduce the use of Accelerometer Sensor to detect changes in the anterior-posterior diameter of the chest; whereas these changes provide an accurate measurement of respiration rate as well as tidal volume variability. The complete system was comprised of wearable calibrated Accelerometer Sensor, Bluetooth Low Energy (BLE) and cloud database. The experiments are conducted with 8 subjects and the overall error in respiration rate calculation is obtained 0.2% considering SPR-BTA spirometer as the reference. We also present a method for Tidal Volume variability (TVvar) estimation while validated using Pearson correlation. The mean value of the correlation coefficient between TVvar derived from the Accelerometer and spirometer for all subjects and three breath patterns is 0.87 which shows a high correspondence of two signals. Furthermore, the results indicate that the Accelerometer driven TVvar achieves the average MSE 1.6E-03±3.69E-03 compared to the reference.