A Novel Biomechanical Model-Aided IMU/UWB Fusion for Magnetometer-Free Lower Body Motion Capture

Zihajehzadeh, Shaghayegh; Park, Edward J.

doi:10.1109/tsmc.2016.2521823

Cited by 72 publications

(92 citation statements)

References 43 publications

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“…Its dimension (7.6cm × 8.0cm × 1.6cm) and weight (58g) are suitable for micro unmanned aerial vehicles. Our UWB ranging algorithm uses two-way TOA measurement to calculate the range shown in (10), which is able to provide relatively steady range measurements and has the ranging area of 100m with ranging error within η = 0.2m. The IMU sensor is from myAHRS+ (altitude heading reference system), which is a low cost high performance attitude and heading reference system (AHRS) containing a 3-axis 16-bit gyroscope, a 3-axis 16-bit accelerometer, and a 3-axis 13-bit magnetometer.…”

Section: A Hardwarementioning

confidence: 99%

Graph Optimization Approach to Range-Based Localization

Wang

Nguyen

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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In this paper, we propose a general graph optimization based framework for localization, which can accommodate different types of measurements with varying measurement time intervals. Special emphasis will be on range-based localization. Range and trajectory smoothness constraints are constructed in a position graph, then the robot trajectory over a sliding window is estimated by a graph based optimization algorithm. Moreover, convergence analysis of the algorithm is provided, and the effects of the number of iterations and window size in the optimization on the localization accuracy are analyzed. Extensive experiments on quadcopter under a variety of scenarios verify the effectiveness of the proposed algorithm and demonstrate a much higher localization accuracy than the existing range-based localization methods, especially in the altitude direction.Index Terms-Graph optimization approach, Range-based localization, 2-D and 3-D spaces, Ultra-wide band radio.

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Section: A Hardwarementioning

confidence: 99%

Graph Optimization Approach to Range-Based Localization

Wang

Nguyen

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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“…Ghasemzadeh et al [35] introduced a novel classification model that identified physical movements from body-worn inertial sensors while considering the collaborative nature and limited resources of the system. Inertial sensors are integrated with UWB localization system in [23] for simultaneous 3-D trajectory tracking and lower body motion capture (MoCap) under various dynamic activities such as walking and jumping.…”

Section: Hmt Systems and Applicationsmentioning

confidence: 99%

“…In essence, human motion tracking can be viewed as a local multi-target real-time high-precision three-dimensional positioning problem [23]. Ultra-Wideband (UWB)-based time-of-arrival (TOA) ranging is the most commonly used high-precision localization technology, its measurement accuracy can reach the centimeter level, and it does not have the drawback of accumulative errors, compared with inertial-sensor-based HMT systems [23,25]. The size of TOA chip is small enough to be integrated into wearable devices.…”

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confidence: 99%

“…Some achievements on IMU/TOA fusion have been reported in many studies (e.g., [20,21,23,25]) However, state-of-the-art studies, (e.g., [23,27]), mainly face the following two drawbacks:…”

mentioning

confidence: 99%

“…Requirements of fixed external anchors: They need to be deployed in certain scenarios; for example, Zihajehzadeh et al [23] introduced a magnetometer-free algorithm for human lower-body motion tracking by fusing inertial sensors with an UWB localization system. However, it is hard to realize wearable systems, and is not suitable for BAN applications.…”

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confidence: 99%

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Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Zhang

et al. 2019

Electronics

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Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér-Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.

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Sensor network oriented human motion capture via wearable intelligent system

Qiu

Zhao

Jiang

et al. 2021

Int J of Intelligent Sys

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Using inertial measurement units mounted on foot is a feasible approach to improve the positioning accuracy for the human motion capture system. This paper presents a lightweight and low cost wireless inertial motion capture system for the simultaneous reconstruction of human body attitude and displacement. First of all, the device is based on human sensor networks and distributes 15 sensor nodes on the key human limbs. Then, after an initial sensor alignment with the reduced error, a zero-speed update algorithm is used to calculate foot displacement. In addition, to constantly update the human posture information, a kind of motion reconstruction method based on the gradient descent method was used to fuse the sensor data. Finally, a new method of three-dimensional human body reconstruction is proposed, which is different from the traditional motion capture system. Through unconstrained traversal of the root, the human posture and foot trajectory are combined to realize the synchronous reconstruction of posture and displacement. It is concluded from the experiment results that the estimation errors are well controlled, and motion patterns are consistent with the actual situation.

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A Novel Biomechanical Model-Aided IMU/UWB Fusion for Magnetometer-Free Lower Body Motion Capture

Cited by 72 publications

References 43 publications

Graph Optimization Approach to Range-Based Localization

Graph Optimization Approach to Range-Based Localization

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Sensor network oriented human motion capture via wearable intelligent system

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