Full-pose estimation using inertial and magnetic sensor fusion in structurized magnetic field for hand motion tracking

Shen, Hui-Min; Lian, Chong; Wu, Xiang-Wei; Bian, Feng; Ping, Yu; Yang, Geng

doi:10.1016/j.measurement.2020.108697

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…Brodie et al used a similar multimodal motion capture system to extract key performance indicators related to athletes' body and physiological limits, such as average drag coefficient and maximum tilt angle during turns, which help optimize game strategies [33]. Hui-Min Shen et al proposed a Magnetic Measurement Unit (MMU) approach for multisensor fusion in motion capture, enhancing system output accuracy and stability [67]. Additionally, Hasegawa et al developed an action-assisted monitoring system that utilizes sound wave information to monitor and provide feedback on the skier's center of gravity, assisting skiers in overcoming instinctive reactions of leaning back [68].…”

Section: Other Motion Capture Systemsmentioning

confidence: 99%

Motion Capture Technology in Sports Scenarios: A Survey

Suo,

Tang,

2024

Sensors

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Motion capture technology plays a crucial role in optimizing athletes’ skills, techniques, and strategies by providing detailed feedback on motion data. This article presents a comprehensive survey aimed at guiding researchers in selecting the most suitable motion capture technology for sports science investigations. By comparing and analyzing the characters and applications of different motion capture technologies in sports scenarios, it is observed that cinematography motion capture technology remains the gold standard in biomechanical analysis and continues to dominate sports research applications. Wearable sensor-based motion capture technology has gained significant traction in specialized areas such as winter sports, owing to its reliable system performance. Computer vision-based motion capture technology has made significant advancements in recognition accuracy and system reliability, enabling its application in various sports scenarios, from single-person technique analysis to multi-person tactical analysis. Moreover, the emerging field of multimodal motion capture technology, which harmonizes data from various sources with the integration of artificial intelligence, has proven to be a robust research method for complex scenarios. A comprehensive review of the literature from the past 10 years underscores the increasing significance of motion capture technology in sports, with a notable shift from laboratory research to practical training applications on sports fields. Future developments in this field should prioritize research and technological advancements that cater to practical sports scenarios, addressing challenges such as occlusion, outdoor capture, and real-time feedback.

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Section: Other Motion Capture Systemsmentioning

confidence: 99%

Motion Capture Technology in Sports Scenarios: A Survey

Suo,

Tang,

2024

Sensors

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“…1) Non-optical Methods: Methods based on electromagnetic transmitters [34], [24], [5], bending [15], [35], [7] or stretch-sensors [6], [2], [16], inertial measurement units [26], [21], [10], or even exoskeletons [31] are often integrated as gloves. Caeiro-Rodríguez et al provide a review of commercial active smart gloves [4].…”

Section: A Motion Capture Of Human Handsmentioning

confidence: 99%

A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

Fabisch¹,

Laux²,

Brust³

2022

Preprint

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Manipulating objects with robotic hands is a complicated task. Not only the pose of the robot's end effector, but also the fingers of the hand need to be controlled and coordinated. Using human demonstrations of movements is an intuitive and data-efficient way of guiding the robot's behavior. We propose a modular framework with an automatic embodiment mapping to transfer human hand motions to robotic systems and use motion capture to record human motion. We evaluate our approach on eight challenging tasks, in which a robotic arm with a mounted robotic hand needs to grasp and manipulate deformable objects or small, fragile material.

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“…Considering that the accelerometers can only compensate for the tilt angles relative to the direction of gravity, it is necessary to introduce magnetometers to provide compensation for the yaw angles, which is known as nine-axis IMU or the magnetic, angular rate, and gravity (MARG) system [14]. Data fusion of three inertial sensors can be achieved by adopting either a complementary filter [14][15][16][17] or a classic Kalman filter [18,19]. The Kalman filter has been widely used as an orientation filter for the majority of the proposed methods.…”

Section: Introductionmentioning

confidence: 99%

IMU/UWB Fusion Method Using a Complementary Filter and a Kalman Filter for Hybrid Upper Limb Motion Estimation

Shi

Zhang

et al. 2023

Sensors

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Motion capture systems have enormously benefited the research into human–computer interaction in the aerospace field. Given the high cost and susceptibility to lighting conditions of optical motion capture systems, as well as considering the drift in IMU sensors, this paper utilizes a fusion approach with low-cost wearable sensors for hybrid upper limb motion tracking. We propose a novel algorithm that combines the fourth-order Runge–Kutta (RK4) Madgwick complementary orientation filter and the Kalman filter for motion estimation through the data fusion of an inertial measurement unit (IMU) and an ultrawideband (UWB). The Madgwick RK4 orientation filter is used to compensate gyroscope drift through the optimal fusion of a magnetic, angular rate, and gravity (MARG) system, without requiring knowledge of noise distribution for implementation. Then, considering the error distribution provided by the UWB system, we employ a Kalman filter to estimate and fuse the UWB measurements to further reduce the drift error. Adopting the cube distribution of four anchors, the drift-free position obtained by the UWB localization Kalman filter is used to fuse the position calculated by IMU. The proposed algorithm has been tested by various movements and has demonstrated an average decrease in the RMSE of 1.2 cm from the IMU method to IMU/UWB fusion method. The experimental results represent the high feasibility and stability of our proposed algorithm for accurately tracking the movements of human upper limbs.

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Full-pose estimation using inertial and magnetic sensor fusion in structurized magnetic field for hand motion tracking

Cited by 8 publications

References 28 publications

Motion Capture Technology in Sports Scenarios: A Survey

Motion Capture Technology in Sports Scenarios: A Survey

A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

IMU/UWB Fusion Method Using a Complementary Filter and a Kalman Filter for Hybrid Upper Limb Motion Estimation

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