scholarly journals Predicting Missing Markers in Real-Time Optical Motion Capture

2009 ◽  
pp. 125-136 ◽  
Author(s):  
Tommaso Piazza ◽  
Johan Lundström ◽  
Andreas Kunz ◽  
Morten Fjeld
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Optical Motion ◽  
Optical Motion Capture

Toward accurate real-time marker labeling for live optical motion capture

2017 ◽  
Vol 33 (6-8) ◽  
pp. 993-1003 ◽  
Author(s):  
Shihong Xia ◽  
Le Su ◽  
Xinyu Fei ◽  
Han Wang
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Time Marker ◽  
Optical Motion ◽  
Optical Motion Capture

Real-Time Optical Motion Capture Balance Sonification System

2020 ◽  
Author(s):  
Mitchell Tillman ◽  
Luke Dahl ◽  
Christopher B. Knowlton ◽  
Antonia Zaferiou
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Optical Motion ◽  
Optical Motion Capture

Real-time marker prediction and CoR estimation in optical motion capture

2012 ◽  
Vol 29 (1) ◽  
pp. 7-26 ◽  
Author(s):  
Andreas Aristidou ◽  
Joan Lasenby
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Time Marker ◽  
Optical Motion ◽  
Optical Motion Capture

scholarly journals An open-source and wearable system for measuring 3D human motion in real-time

2021 ◽  
Author(s):  
Patrick Slade ◽  
Ayman Habib ◽  
Jennifer L. Hicks ◽  
Scott L. Delp
Keyword(s):  
Open Source ◽  
Real Time ◽  
Motion Capture ◽  
Human Motion ◽  
Sensor Systems ◽  
Wearable Sensor ◽  
Joint Angles ◽  
Wearable System ◽  
Optical Motion ◽  
Optical Motion Capture

AbstractAnalyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation interventions for conditions such as osteoarthritis, stroke, and Parkinson’s disease. Optical motion capture systems are the current standard for estimating kinematics but require expensive equipment located in a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Further, many wearable sensor systems require a computer in close proximity and rely on proprietary software, making it difficult for researchers to reproduce experimental findings. Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller. We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking (n = 5) and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task (n = 5). The open-source software and hardware are scalable, tracking between 1 and 14 body segments, with one sensor per segment. Kinematics are estimated in real-time using a musculoskeletal model and inverse kinematics solver. The computation frequency, depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track up to 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $20 for each tracked segment. The OpenSenseRT system is accurate, low-cost, and simple to replicate, enabling movement analysis in labs, clinics, homes, and free-living settings.

scholarly journals Modeling and Animating Virtual Humans for Real-Time Applications

2007 ◽  
Vol 6 (4) ◽  
pp. 11-20 ◽  
Author(s):  
Frank Hülsken ◽  
Christian Eckes ◽  
Roland Kuck ◽  
Jörg Unterberg ◽  
Sophie J�rg
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
3D Models ◽  
Rigid Bodies ◽  
Whole Body ◽  
Virtual Character ◽  
Real People ◽  
Optical Motion ◽  
High Resolution Models ◽  
Optical Motion Capture

We report on the workflow for the creation of realistic virtual anthropomorphic characters. 3D-models of human heads have been reconstructed from real people by following a structured light approach to 3D-reconstruction. We describe how these high-resolution models have been simplified and articulated with blend shape and mesh skinning techniques to ensure real-time animation. The full-body models have been created manually based on photographs. We present a system for capturing whole body motions, including the fingers, based on an optical motion capture system with 6 DOF rigid bodies and cybergloves. The motion capture data was processed in one system, mapped to a virtual character and visualized in real-time. We developed tools and methods for quick post processing. To demonstrate the viability of our system, we captured a library consisting of more than 90 gestures.

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