Fast calibration of a 9-DOF IMU using a 3 DOF position tracker and a semi-random motion sequence

Yadav, Nagesh; Bleakley, Chris J.

doi:10.1016/j.measurement.2016.04.066

Cited by 13 publications

(9 citation statements)

References 7 publications

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“…The Shimmer, a commercially available IMU, was selected for use in this research due to its small and lightweight design, data logging capacity, ample battery life, and previous use in related work [8] [9]. Using an internal SD card, the Shimmer can simultaneously record accelerometer, gyroscope, and magnetometer data for extended periods.…”

Section: Uptime Calculation -Imu Sensor Fusionmentioning

confidence: 99%

Accurate and Objective Determination of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Disease Severity with a Wearable Sensor

Palombo

Campos

Vernon

et al. 2020

Preprint

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Background Approximately 2.5 million people in the U.S. suffer from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This disease negatively impacts patients’ ability to function, often resulting in difficulty maintaining employment, sustaining financial independence, engaging socially with others, and in particularly severe cases, consistently and adequately performing activities of daily living. The focus of this research was to develop a sensor-based method to measure upright activity defined as time with feet on the floor and referred to as UpTime, as an indicator of ME/CFS disease severity.Methods A commercially available inertial measurement unit (IMU), the Shimmer, was selected for this research. A Kalman filter was used to convert IMU data collected by the Shimmer to angle estimates. Angle estimate accuracy was confirmed by comparison to a motion capture system. Leg angle estimates were then converted to personalized daily UpTime scores using a critical angle of 39º from vertical to differentiate between upright (feet on the floor) and not upright. A six-day, case-control study with 15 subjects (five healthy controls, five moderate-level ME/CFS, and five severe-level ME/CFS) was conducted to determine the utility of UpTime for assessing disease severity.Results UpTime was found to be a significant measure of ME/CFS disease severity. Severely ill ME/CFS patients spend less than 20% of each day with feet on the floor. Moderately ill ME/CFS patients spend between 20–30% of each day with feet on the floor. Healthy controls have greater than 30% UpTime. IMU-measured UpTime was more precise than self-reported hours of upright activity which were over-estimated by patients.Conclusions UpTime is an accurate and objective measure of upright activity, a measure that can be used to assess disease severity in ME/CFS patients. Due to its ability to accurately monitor upright activity, UpTime can also be used as a reliable endpoint for evaluating ME/CFS treatment efficacy. Future studies with larger samples and extended data collection periods are required to fully confirm the use of UpTime as a measure of disease severity in ME/CFS. With the added perspective of large-scale studies, this sensor-based platform could provide a recovery path for individuals struggling with ME/CFS.

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Section: Uptime Calculation -Imu Sensor Fusionmentioning

confidence: 99%

Accurate and Objective Determination of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Disease Severity with a Wearable Sensor

Palombo

Campos

Vernon

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The Shimmer, a commercially available IMU, was selected for use in this research due to its small and lightweight design, data logging capacity, ample battery life, and previous use in related work [7] [8]. Using an internal SD card, the Shimmer can simultaneously record accelerometer, gyroscope, and magnetometer data for extended periods.…”

Section: Uptime Calculation -Imu Sensor Fusionmentioning

confidence: 99%

Accurate and Objective Determination of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Disease Severity with a Wearable Sensor

Palombo

Campos

Vernon

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Approximately 2.5 million people in the U.S. suffer from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This disease negatively impacts patients’ ability to function, often resulting in difficulty maintaining employment, sustaining financial independence, engaging socially with others, and in particularly severe cases, consistently and adequately performing activities of daily living. The focus of this research was to develop a sensor-based method to measure upright activity defined as time with feet on the floor and referred to as UpTime, as an indicator of ME/CFS disease severity. Methods A commercially available inertial measurement unit (IMU), the Shimmer, was selected for this research. A Kalman filter was used to convert IMU data collected by the Shimmer to angle estimates. Angle estimate accuracy was confirmed by comparison to a motion capture system. Leg angle estimates were then converted to personalized daily UpTime scores using a critical angle of 39º from vertical to differentiate between upright (feet on the floor) and not upright. A six-day, case-control study with 15 subjects (five healthy controls, five moderate-level ME/CFS, and five severe-level ME/CFS) was conducted to determine the utility of UpTime for assessing disease severity. Results UpTime was found to be a significant measure of ME/CFS disease severity. Severely ill ME/CFS patients spend less than 20% of each day with feet on the floor. Moderately ill ME/CFS patients spend between 20-30% of each day with feet on the floor. Healthy controls have greater than 30% UpTime. IMU-measured UpTime was more precise than self-reported hours of upright activity which were over-estimated by patients. Conclusions UpTime is an accurate and objective measure of upright activity, a measure that can be used to assess disease severity in ME/CFS patients. Due to its ability to accurately monitor upright activity, UpTime can also be used as a reliable endpoint for evaluating ME/CFS treatment efficacy. Future studies with larger samples and extended data collection periods are required to fully confirm the use of UpTime as a measure of disease severity in ME/CFS. With the added perspective of large-scale studies, this sensor-based platform could provide a recovery path for individuals struggling with ME/CFS.

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“…According to the embedded navigation application of MEMS inertial sensor and Micro IMU in high dynamic environment, the performance and precision of MEMS inertial sensor and Micro IMU are improved based on error compensation methods [1]. Among them, most calibration methods are developed to eliminate the adverse effects of the deterministic and systematic errors [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…For example, a coarse estimation and postparameter estimation calibration methods are proposed. The effectiveness of the calibration method was evaluated by estimating heading and inclination using the calibrated IMU [1]. For low cost micro IMU, an easy selfcalibration method is proposed utilizing the accelerometers as level datum for calibrating gyros [3].…”

Section: Introductionmentioning

confidence: 99%

Compounded Calibration Based on FNN and Attitude Estimation Method Using Intelligent Filtering for Low Cost MEMS Sensor Application

Lei

Guan

2019

Mathematical Problems in Engineering

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Micro electro mechanical system (MEMS) inertial sensors have advantages, including small size and low power consumption. The performances of Micro Inertial measurement unit (IMU), which is composed of MEMS inertial sensors, degrade, and error, will become larger in high dynamic environment. In order to solve the problem, a novel combined calibration method for compensating the deterministic error of MEMS sensors is proposed. Considering the rotation of different sensitive axes in high dynamic and low dynamic environment, the compounded calibration based on fuzzy neural network (FNN) is adopted to identify the coupling coefficients to eliminate the adverse coupling effects between different rotation axes. Furthermore, the self-developed Micro IMU and magnetometer are applied in attitude estimation system. Considering the large attitude error occurred in most cases, the approach utilizing the estimation of error quaternion vector could avoid the calculation error due to inaccurate modeling in the skew symmetric matrix that comprises attitude error vector components. The intelligent Kalman filter (IKF) based on complexity state equation of error quaternion is designed to improve the performance by adjusting the parameters of filter on line. The experimental results show that the proposed approach could have a higher level of stability and accuracy in comparison to other attitude estimation algorithms.

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Fast calibration of a 9-DOF IMU using a 3 DOF position tracker and a semi-random motion sequence

Cited by 13 publications

References 7 publications

Accurate and Objective Determination of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Disease Severity with a Wearable Sensor

Accurate and Objective Determination of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Disease Severity with a Wearable Sensor

Accurate and Objective Determination of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Disease Severity with a Wearable Sensor

Compounded Calibration Based on FNN and Attitude Estimation Method Using Intelligent Filtering for Low Cost MEMS Sensor Application

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