Orientation Estimation Using Filter-Based Inertial Data Fusion for Posture Recognition

Segarra, David; Caballeros, Jessica; Aguilar, Wilbert G.; Samà, Albert; Rodríguez-Martín, Daniel

doi:10.1007/978-3-030-14094-6_15

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Internal factors affecting an IMU’s accuracy include the complex process of sensor fusion that is required to determine the device’s orientation with respect to global space [8]. Team sports are intuitively observed and described in an earth-related coordinate system, which can be defined by the z-axis running parallel to earth’s gravitational acceleration and the horizontal axes being in accordance with the field’s dimensions.…”

Section: Introductionmentioning

confidence: 99%

Validation of Wearable Sensors during Team Sport-Specific Movements in Indoor Environments

Roell¹,

Mahler²,

Lienhard³

et al. 2019

Sensors

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The aim of this study was to determine possible influences, including data processing and sport-specific demands, on the validity of acceleration measures by an inertial measurement unit (IMU) in indoor environments. IMU outputs were compared to a three-dimensional (3D) motion analysis (MA) system and processed with two sensor fusion algorithms (Kalman filter, KF; Complementary filter, CF) at temporal resolutions of 100, 10, and 5 Hz. Athletes performed six team sport-specific movements whilst wearing a single IMU. Mean and peak acceleration magnitudes were analyzed. Over all trials (n = 1093), KF data overestimated MA resultant acceleration by 0.42 ± 0.31 m∙s−2 for mean and 4.18 ± 3.68 m∙s−2 for peak values, while CF processing showed errors of up to 0.57 ± 0.41 m∙s−2 and −2.31 ± 2.25 m∙s−2, respectively. Resampling to 5 Hz decreased the absolute error by about 14% for mean and 56% for peak values. Still, higher acceleration magnitudes led to a large increase in error. These results indicate that IMUs can be used for assessing accelerations in indoor team sports with acceptable means. Application of a CF and resampling to 5 Hz is recommended. High-acceleration magnitudes impair validity to a large degree and should be interpreted with caution.

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Section: Introductionmentioning

confidence: 99%

Validation of Wearable Sensors during Team Sport-Specific Movements in Indoor Environments

Roell¹,

Mahler²,

Lienhard³

et al. 2019

Sensors

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show abstract

“…Gravina's team designed an information fusion‐based Dempster–Shafer (D‐S) theory using body sensor networks (BSNs), and the usability of D‐S in the field of posture detection was experimentally demonstrated 31 . Segerra et al used sensor fusion data with gyroscopes and accelerometers to design an inertial data estimation orientation model based on Kalman filtering with Mahony filtering, which experimentally demonstrated the excellence of Kalman filtering, but upfront subjects had to be individually preprocessed to correct for bias 32 . In 2020, Permatasari et al's team optimized the gait recognition problem by using the accelerometer and gyroscope data to encode covariance matrices, and since nonstar covariance matrices are symmetric positive definite matrices, the SPD matrix was designed as a feature fusion of point pairs of data in the Riemannian‐plane, which experimentally proved to be effective in overcoming the large data sets required in traditional gait recognition and the long computation time problem 33 .…”

Section: Related Workmentioning

confidence: 99%

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Liu

Jiang

et al. 2021

Int J Intell Syst

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Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective rehabilitation evaluation. In this paper, six gestures of upper limb rehabilitation were monitored and collected using microelectromechanical systems sensors, where data stability was guaranteed using data preprocessing methods, that is, deweighting, interpolation, and feature extraction. A fully connected neural network has been proposed investigating the effects of different hidden layers, and determining its activation functions and optimizers. Experiments have depicted that a three‐hidden‐layer model with a softmax function and an adaptive gradient descent optimizer can reach an average gesture recognition rate of 97.19%. A stop mechanism has been used via recognition of dangerous gesture to ensure the safety of the system, and the lightweight cryptography has been used via hash to ensure the security of the system. Comparison to the classification models, for example, k‐nearest neighbor, logistic regression, and other random gradient descent algorithms, was conducted to verify the outperformance in recognition of upper limb gesture data. This study also provides an approach to creating health profiles based on large‐scale rehabilitation data and therefore consequent diagnosis of the effects of FES rehabilitation.

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An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data

Liu

et al. 2021

2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

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Orientation Estimation Using Filter-Based Inertial Data Fusion for Posture Recognition

Cited by 4 publications

References 9 publications

Validation of Wearable Sensors during Team Sport-Specific Movements in Indoor Environments

Validation of Wearable Sensors during Team Sport-Specific Movements in Indoor Environments

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data

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