Estimating Three-Dimensional Body Orientation Based on an Improved Complementary Filter for Human Motion Tracking

Yi, Chunzhi; Ma, Jiaxing; Guo, Hao; Han, Jiahong; Gao, Hefu; Jiang, Feng; Yang, Chengxiang

doi:10.3390/s18113765

Cited by 36 publications

(25 citation statements)

References 22 publications

(35 reference statements)

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“…Derived orientation is a result of the minimization of predicted and estimated IMU values using a two-stage gradient-descent optimization algorithm. This open-source fusion algorithm is computationally efficient and has been used as a benchmark to evaluate other orientation estimation algorithms, thus used in our study [ 16 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

Efficacy of Inertial Measurement Units in the Evaluation of Trunk and Hand Kinematics in Baseball Hitting

Punchihewa

Miyazaki

Chosa

et al. 2020

Sensors

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Baseball hitting is a highly dynamic activity, and advanced methods are required to accurately obtain biomechanical data. Inertial measurement units (IMUs) can capture the motion of body segments at high sampling rates both indoor and outdoor. The bat rotates around the longitudinal axis of the body; thus, trunk motion plays a key role in baseball hitting. Segmental coordination is important in transferring power to a moving ball and, therefore, useful in evaluating swing kinematics. The current study aimed to investigate the validity and reliability of IMUs with a sampling rate of 1000 Hz attached on the pelvis, thorax, and hand in assessing trunk and hand motion during baseball hitting. Results obtained using the IMU and optical motion capture system (OMCS) were compared. Angular displacements of the trunk segments and spine joint had a root mean square error of <5°. The mean absolute error of the angular velocities was ≤5%. The intra-class correlation coefficient (>0.950) had excellent reliability for trunk kinematics along the longitudinal-axis. Hand velocities at peak and impact corresponded to the values determined using the OMCS. In conclusion, IMUs with high sampling rates are effective in evaluating trunk and hand movement coordination during hitting motion.

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Section: Methodsmentioning

confidence: 99%

Efficacy of Inertial Measurement Units in the Evaluation of Trunk and Hand Kinematics in Baseball Hitting

Punchihewa

Miyazaki

Chosa

et al. 2020

Sensors

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“…We next implement the absolute orientation estimating methods adopted from [ 22 ] in order to estimate the absolute orientations of IMU2 and IMU3. The estimates are denoted by rotation matrices, i.e.,

for the absolute orientations of IMU2 and

for absolute orientations of IMU3.…”

Section: Methodsmentioning

confidence: 99%

“…To be specific, firstly the raw IMU measurements are used to estimate the relative orientation of sensors with the reference of the Earth frame. The so-called “absolute orientations” of IMUs mounted on two adjacent limb segments are estimated following the methods proposed in inertial motion tracking-related literatures [ 19 , 20 , 21 , 22 ]. More precisely, the orientations of sensor-fixed coordinate frames with respect to a reference frame, i.e., the Earth coordinate frame, are estimated.…”

Section: Introductionmentioning

confidence: 99%

Reference Frame Unification of IMU-Based Joint Angle Estimation: The Experimental Investigation and a Novel Method

Jiang

Yang

et al. 2021

Sensors

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Inertial measurement unit (IMU)-based joint angle estimation is an increasingly mature technique that has a broad range of applications in clinics, biomechanics and robotics. However, the deviations of different IMUs’ reference frames, referring to IMUs’ individual orientations estimating errors, is still a challenge for improving the angle estimation accuracy due to conceptual confusion, relatively simple metrics and the lack of systematical investigation. In this paper, we clarify the determination of reference frame unification, experimentally study the time-varying characteristics of reference frames’ deviations and accordingly propose a novel method with a comprehensive metric to unify reference frames. To be specific, we firstly define the reference frame unification (RFU) and distinguish it with drift correction that has always been confused with the term RFU. Secondly, we design a mechanical gimbal-based experiment to study the deviations, where sensor-to-body alignment and rotation-caused differences of orientations are excluded. Thirdly, based on the findings of the experiment, we propose a novel method to utilize the consistency of the joint axis under the hinge-joint constraint, gravity acceleration and local magnetic field to comprehensively unify reference frames, which meets the nonlinear time-varying characteristics of the deviations. The results on ten human subjects reveal the feasibility of our proposed method and the improvement from previous methods. This work contributes to a relatively new perspective of considering and improving the accuracy of IMU-based joint angle estimation.

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“…Inertial measurement unit (combination of accelerometers, gyroscopes, and magnetometers) is widely used in the field of robotics to help localization algorithms. The received data can be analyzed: motion measurement of robotics arm or mobile robots [12,13], tracking people or robotic systems [14,15], gait analysis [16,17], and inertial navigation or positioning for mobile robots [18,19]. Its affordability and ease of use on multiple robotic platforms make its integration in robot swarm possible [20].…”

Section: Related Workmentioning

confidence: 99%

Toward a robot swarm protecting a group of migrants

Vaidis

Otis

2020

Intel Serv Robotics

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Different geopolitical conflicts of recent years have led to mass migration of several civilian populations. These migrations take place in militarized zones, indicating real danger contexts for the populations. Indeed, civilians are increasingly targeted during military assaults. Defense and security needs have increased; therefore, there is a need to prioritize the protection of migrants. Very few or no arrangements are available to manage the scale of displacement and the protection of civilians during migration. In order to increase their security during mass migration in an inhospitable territory, this article proposes an assistive system using a team of mobile robots, labeled a rover swarm that is able to provide safety area around the migrants. We suggest a coordination algorithm including CNN and fuzzy logic that allows the swarm to synchronize their movements and provide better sensor coverage of the environment. Implementation is carried out using on a reduced scale rover to enable evaluation of the functionalities of the suggested software architecture and algorithms. Results bring new perspectives to helping and protecting migrants with a swarm that evolves in a complex and dynamic environment.

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Estimating Three-Dimensional Body Orientation Based on an Improved Complementary Filter for Human Motion Tracking

Cited by 36 publications

References 22 publications

Efficacy of Inertial Measurement Units in the Evaluation of Trunk and Hand Kinematics in Baseball Hitting

Efficacy of Inertial Measurement Units in the Evaluation of Trunk and Hand Kinematics in Baseball Hitting

Reference Frame Unification of IMU-Based Joint Angle Estimation: The Experimental Investigation and a Novel Method

Toward a robot swarm protecting a group of migrants

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