5/15/2023 0 Comments Synchronizacja danychELECTRICAL and ELECTRONIC ENGINEERING Read more The approach presented can be used for various applications to generate natural expressive and realistic facial animation, for example, generating a personal avatar for different users. Our experiments demonstrate that the approach presented can effectively synthesize realistic expressive facial animation. C3D format are mapped to a 3D animated face using Autodesk MotionBuilder 2009. After that, the resulting data from Matlab is converted to. The quality of the resulting data is evaluated by computing the Root Mean Square Error. Programs are written using Matlab to perform dimensionality reduction techniques on the data. The purpose of reduce the dimensions of the data is to prepare them to be used in the construction of the Phoneme-Independent Expression Eigenspace (PIEES) in the modeling stage. facial motion capture data with different emotions are collected using Vicon Nexus are reduced using dimensionality reduction techniques. Data captured from real speaker are used which would make the mapped facial animation more natural and lifelike. In this project, a realistic and expressive computer facial animation system is developed by automated learning from Vicon system facial motion capture data. In the future the proposed system will allow the creation of a database of inertial data from human gait with accurate ground truth synchronization.Ĭomputer facial animation still remains a very challenging topic within the computer graphics community. Recording was highly synchronous and the collected kinematics had a correlation of up to 0.99. Evaluation of delay and jitter of the system showed a mean delay of 2 ms and low jitter of 20 us. Inertial sensor data were recorded during walking and running with the shoe, while kinematic and kinetic ground truth was acquired from the synchronized VICON system. To demonstrate the applicability of the system for mobile gait analysis, a Shimmer wireless sensor node with inertial sensors was mounted at the heel of a running shoe and synchronized with an external VICON motion capturing system using the wireless trigger system. In this paper we present a wireless trigger system which allows reliable synchronization of wearable sensors to external systems providing ground truth. However, development of algorithms for this task requires kinematic data with accurate and highly synchronous ground truth. Mobile gait analysis focuses on the automatic extraction of gait parameters from wearable sensor data. The large range and variability in delay times between trials highlights the need for synchronization on a continual basis, rather than application of an average or constant value to correct for time delays between systems. During the reach-to-grasp trials, delays between the data collection systems ranged from 4 ms to 235 ms. Results are provided to validate and demonstrate the accuracy of the synchronization of motion capture with other data collection systems. An application of the synchronization method is demonstrated using biomechanical data collected during a rapid reach-to-grasp reaction, where data from motion capture and load cells are collected. This paper describes a novel method for synchronizing multiple data collection instruments including load cells and a motion capture system, using a common analog signal. Synchronization of multiple data collection systems is necessary for accurate temporal alignment of data, and is particularly important when considering rapid movements which occur in less than one second. To demonstrate continuous sensing, two devices are networked for a period of two hours to identify differences between sedentary and active comnortments. To demonstrate sensing accuracy, three devices are strapped to the motion performer's arm to record the articulated motion of a hand wave gesture. The proposed framework explores solutions for sensor fusion, data synchronization, data streaming and remote control functionality in smartphones. The goal of this research is to present a modular methodology for amalgamating smartphone sensor data within a centralized repository that is suitable for experimental research. Data is transferred in real-time using the motion cloud, an online gateway and storehouse for inertial data. This paper introduces a framework that networks smartphone devices to produce body sensor networks for motion capture and activity tracking application areas. Because of advances in inertial microelectronics and mobile computing technologies, highly accurate sensor hardware has become ubiquitous in modern smartphones.
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