The Experts below are selected from a list of 5067 Experts worldwide ranked by ideXlab platform
Zeljko Zilic - One of the best experts on this subject based on the ideXlab platform.
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MobiHealth - Tidal volume variability and respiration rate estimation using a wearable Accelerometer Sensor
2014Co-Authors: Atena Roshan Fekr, Katarzyna Radecka, Zeljko ZilicAbstract: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.
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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, 2014Co-Authors: Atena Roshan Fekr, Katarzyna Radecka, Zeljko ZilicAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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SMC - Motion recognition with smart phone embedded 3-axis Accelerometer Sensor
2012 IEEE International Conference on Systems Man and Cybernetics (SMC), 2012Co-Authors: Jinsuk Baek, Paul S. FisherAbstract: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.
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Motion recognition with smart phone embedded 3-axis Accelerometer Sensor
2012 IEEE International Conference on Systems Man and Cybernetics (SMC), 2012Co-Authors: Jinsuk Baek, Paul S. FisherAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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MobiHealth - Tidal volume variability and respiration rate estimation using a wearable Accelerometer Sensor
2014Co-Authors: Atena Roshan Fekr, Katarzyna Radecka, Zeljko ZilicAbstract: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.
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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, 2014Co-Authors: Atena Roshan Fekr, Katarzyna Radecka, Zeljko ZilicAbstract: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.
Jang-ho Choi - One of the best experts on this subject based on the ideXlab platform.
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Real-time distance awareness algorithm based on wireless signal strength and Accelerometer Sensor of smart device in an indoor environment
5th IEEE International Conference on Consumer Electronics - Berlin ICCE-Berlin 2015, 2016Co-Authors: Joon Young Jung, Dong Oh Kang, Jang-ho Choi, Chang Seok BaeAbstract:© 2015 IEEE.When mobile devices use the distance between devices in an application, the distance should be measured easily and accurately. The RSSI system can be used to estimate the distance easily and inexpensively because most devices can estimate the received signal strength. However, estimating the distance using a RSSI is difficult due to inaccuracies. Therefore, we propose distance awareness algorithm based on the RSSI and Accelerometer Sensor suitable for real-time distance estimation in an indoor environment. To evaluate the effectiveness of the proposed algorithm, we tested the distance measurement in an indoor hall environment. The results of this test indicate that the proposed algorithm does not only reduces the inaccuracy of Bluetooth RSSI significantly, but also estimates real-time distance more accurately using an Accelerometer Sensor.
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ICCE-Berlin - Real-time distance awareness algorithm based on wireless signal strength and Accelerometer Sensor of smart device in an indoor environment
2015 IEEE 5th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), 2015Co-Authors: Joon Young Jung, Dong Oh Kang, Jang-ho ChoiAbstract:When mobile devices use the distance between devices in an application, the distance should be measured easily and accurately. The RSSI system can be used to estimate the distance easily and inexpensively because most devices can estimate the received signal strength. However, estimating the distance using a RSSI is difficult due to inaccuracies. Therefore, we propose distance awareness algorithm based on the RSSI and Accelerometer Sensor suitable for real-time distance estimation in an indoor environment. To evaluate the effectiveness of the proposed algorithm, we tested the distance measurement in an indoor hall environment. The results of this test indicate that the proposed algorithm does not only reduces the inaccuracy of Bluetooth RSSI significantly, but also estimates real-time distance more accurately using an Accelerometer Sensor.
Joon Young Jung - One of the best experts on this subject based on the ideXlab platform.
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Real-time distance awareness algorithm based on wireless signal strength and Accelerometer Sensor of smart device in an indoor environment
5th IEEE International Conference on Consumer Electronics - Berlin ICCE-Berlin 2015, 2016Co-Authors: Joon Young Jung, Dong Oh Kang, Jang-ho Choi, Chang Seok BaeAbstract:© 2015 IEEE.When mobile devices use the distance between devices in an application, the distance should be measured easily and accurately. The RSSI system can be used to estimate the distance easily and inexpensively because most devices can estimate the received signal strength. However, estimating the distance using a RSSI is difficult due to inaccuracies. Therefore, we propose distance awareness algorithm based on the RSSI and Accelerometer Sensor suitable for real-time distance estimation in an indoor environment. To evaluate the effectiveness of the proposed algorithm, we tested the distance measurement in an indoor hall environment. The results of this test indicate that the proposed algorithm does not only reduces the inaccuracy of Bluetooth RSSI significantly, but also estimates real-time distance more accurately using an Accelerometer Sensor.
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ICCE-Berlin - Real-time distance awareness algorithm based on wireless signal strength and Accelerometer Sensor of smart device in an indoor environment
2015 IEEE 5th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), 2015Co-Authors: Joon Young Jung, Dong Oh Kang, Jang-ho ChoiAbstract:When mobile devices use the distance between devices in an application, the distance should be measured easily and accurately. The RSSI system can be used to estimate the distance easily and inexpensively because most devices can estimate the received signal strength. However, estimating the distance using a RSSI is difficult due to inaccuracies. Therefore, we propose distance awareness algorithm based on the RSSI and Accelerometer Sensor suitable for real-time distance estimation in an indoor environment. To evaluate the effectiveness of the proposed algorithm, we tested the distance measurement in an indoor hall environment. The results of this test indicate that the proposed algorithm does not only reduces the inaccuracy of Bluetooth RSSI significantly, but also estimates real-time distance more accurately using an Accelerometer Sensor.
