Real-time motion tracking in image-guided oral implantology

Immersion is one of the most important aspects in ensuring the applicability of Virtual Reality systems to training regimes aiming to improve performance. To ensure that this key aspect is met, the registration of motion between the real world and virtual environment must be made as accurate and as low latency as possible. Thus, an in-house developed Inertial Measurement Unit (IMU) system is developed for use in tracking the movement of the player’s racquet. This IMU tracks 6 DOF motion data and transmits it to the mobile training system for processing. Physically, the custom motion is built into the shape of a racquet grip to give a more natural sensation when swinging the racquet. In addition to that, an adaptive filter framework is also established to cope with different racquet movements automatically, enabling real-time 6 DOF tracking by balancing the jitter and latency. Experiments are performed to compare the efficacy of our approach with other conventional tracking methods such as the using Microsoft Kinect. The results obtained demonstrated noticeable accuracy and lower latency when compared with the aforementioned methods.

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Expectations and Proposals for the Next Generation Robots. Tracking Vision: A real-time motion tracking system.

Journal of the Robotics Society of Japan ◽

10.7210/jrsj.16.52 ◽

1998 ◽

Vol 16 (1) ◽

pp. 52-53

Author(s):

TAKASHI UCHIYAMA

Keyword(s):

Real Time ◽

Motion Tracking ◽

Tracking System ◽

Next Generation ◽

Time Motion ◽

Motion Tracking System

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Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics

Algorithms ◽

10.3390/a13100266 ◽

2020 ◽

Vol 13 (10) ◽

pp. 266

Author(s):

Lorenzo Rapetti ◽

Yeshasvi Tirupachuri ◽

Kourosh Darvish ◽

Stefano Dafarra ◽

Gabriele Nava ◽

...

Keyword(s):

Real Time ◽

Inverse Kinematics ◽

Motion Tracking ◽

Lyapunov Analysis ◽

System Configuration ◽

Method Performance ◽

Model Based ◽

Time Motion ◽

Velocity Correction ◽

Time Critical

This paper contributes towards the development of motion tracking algorithms for time-critical applications, proposing an infrastructure for dynamically solving the inverse kinematics of highly articulate systems such as humans. The method presented is model-based, it makes use of velocity correction and differential kinematics integration in order to compute the system configuration. The convergence of the model towards the measurements is proved using Lyapunov analysis. An experimental scenario, where the motion of a human subject is tracked in static and dynamic configurations, is used to validate the inverse kinematics method performance on human and humanoid models. Moreover, the method is tested on a human-humanoid retargeting scenario, verifying the usability of the computed solution in real-time robotics applications. Our approach is evaluated both in terms of accuracy and computational load, and compared to iterative optimization algorithms.

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