Online human movement classification using wrist-worn wireless sensors

Šarčević, Peter; Kincses, Zoltan; Pletl, Szilveszter

doi:10.1007/s12652-017-0606-1

Cited by 33 publications

(24 citation statements)

References 31 publications

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“…Beside the scaling and misalignment errors (∆S A , ∆S H , M A , and M H ), the magnetometer measurements are disturbed by magnetic soft iron and hard iron errors caused by the local environment, represented by the 3 × 3 matrix B si and the 3 × 1 vector b h , respectively. These model errors are determined via self-calibration procedures which address the time-invariant nature of the vector fields and map the distribution of the measurements on an ellipsoid [63][64][65]. We assume that the compensation has already been performed (therefore, h k := B −1 si h k − b hi ), the bias and scale errors are zero, and the misalignment errors are identity matrices.…”

Section: Accelerometer and Magnetometer Modelsmentioning

confidence: 99%

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

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This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.The MEMS inertial measurement unit (IMU), composed of tri-axis MEMS accelerometer, gyroscope, and magnetometer sensors, also known as the measurement system of magnetic, angular rate, and gravity (MARG) sensor arrays, is the most commonly utilized device to track the real-time orientation of mobile platforms at present. The low-cost, low power consumption, and small size characteristics meet technological requirements, and therefore these devices have been widely utilized in embedded systems, where the filtering algorithm is executed by a microprocessor. As a result, an attitude and heading reference system (AHRS) has been formed, which provides the complete orientation measurement relative to the Earth's gravitational and magnetic fields (global reference system), where the attitude denotes the roll and pitch angles, whereas heading refers to the yaw Euler angle [19]. The role of the aforementioned filtering algorithm is to combine the individual features of each sensor and provide both properly smoothed and robust attitude results with regard to the global reference system, in either Euler angles or quaternions. The most common method applied in sensor fusion techniques synthesizes the short-term accuracy of gyroscope-based attitude realizations and the accelerometer and magnetometer provide rough, low-frequency attitude corrections. This technique cancels the accumulated error (drift), smooths the signals, and produces long-term stable outputs if the IMU is in stationary states. Significant decrease in estimation performance arises when...

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Section: Accelerometer and Magnetometer Modelsmentioning

confidence: 99%

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

Self Cite

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“…An accelerometer is the most commonly used sensor for measuring the motion of the human body. It measures acceleration in one or more axes [ 82 ]. The acceleration can be determined by duty cycles which measure the length of the positive pulse width and period.…”

Section: Sensorsmentioning

confidence: 99%

“…The outputs are digital signals whose duty cycles are proportional to acceleration [ 32 ]. Triaxial accelerometers (which measure vibration in three perpendicular axes) and magnetometers (measuring the strength and direction of magnetic fields) can be used to detect different human arm and body movements [ 82 ], while RGB-D sensors, in combination with triaxial accelerometers, can be used to detect 20 different human activities [ 23 ].…”

Section: Sensorsmentioning

confidence: 99%

“…Gyroscope sensors are used to determine the rotation of different parts of the body. They monitor activity by measuring body rotation and angular velocity around one or more axes while magnetic field sensors are useful in determining orientation relative to the earth’s magnetic north [ 33 , 82 ]. Gyroscopes are mostly used in combination with accelerometers.…”

Section: Sensorsmentioning

confidence: 99%

“…Sarcevic et al [ 82 ] presented a system which recognizes a person’s arm and body movements using wrist-wearable sensors. The inputs were accelerometer, gyroscope, and magnetometer.…”

Section: Intelligent Data Analysismentioning

confidence: 99%

See 2 more Smart Citations

Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review

Kamišalić

Fister

Turkanović

et al. 2018

Sensors

230

133

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Wearable devices have recently received considerable interest due to their great promise for a plethora of applications. Increased research efforts are oriented towards a non-invasive monitoring of human health as well as activity parameters. A wide range of wearable sensors are being developed for real-time non-invasive monitoring. This paper provides a comprehensive review of sensors used in wrist-wearable devices, methods used for the visualization of parameters measured as well as methods used for intelligent analysis of data obtained from wrist-wearable devices. In line with this, the main features of commercial wrist-wearable devices are presented. As a result of this review, a taxonomy of sensors, functionalities, and methods used in non-invasive wrist-wearable devices was assembled.

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Comparison Between Features Extracted in the Time and Frequency Domain with the Triangulation Method in the Recognition of Activities of Human Movements

Giacomossi,

Pichorim

2024

IFMBE Proceedings

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Online human movement classification using wrist-worn wireless sensors

Cited by 33 publications

References 31 publications

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review

Comparison Between Features Extracted in the Time and Frequency Domain with the Triangulation Method in the Recognition of Activities of Human Movements

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