Estimating Human Skeleton Parameters and Configuration in Real-Time from Markered 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.

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Design and implementation of Peking Opera action scoring system based on human skeleton information

MATEC Web of Conferences ◽

10.1051/matecconf/201823201026 ◽

2018 ◽

Vol 232 ◽

pp. 01026 ◽

Cited By ~ 1

Author(s):

Hang Zhao ◽

Youdong Ding ◽

Bing Yu ◽

Chenfeng Jiang ◽

Wanying Zhang

Keyword(s):

Motion Capture ◽

Scoring System ◽

Body Part ◽

Human Action ◽

Human Skeleton ◽

Skeleton Model ◽

Software Interface ◽

Peking Opera ◽

Optical Motion ◽

Optical Motion Capture

At present, most of the preservation records of Peking Opera remain in the ways of video and text, and the digitalization degree is far lower than the development level of science and technology. The immaterial cultural heritage cannot be fully displayed and Peking Opera’s value is weakened. Therefore, adopting advanced motion capture technology is of great significance to the protection and inheritance of Peking Opera. We use optical motion capture equipment to record the movement information of Peking Opera actors, then keep the human skeleton information in a specific file format. After that, the hierarchical human action skeleton model was analysed, and the final score was obtained by comparing the change sequence of information of reference action and training action skeleton with the improved DTW algorithm. We have realized the graphical interface of the system, and the trainer can easily select the action segments to train or select a specific body part for specific action training. This paper introduces the overall design framework of our Peking Opera action scoring system, including the collection of action information, the implementation of scoring algorithm and the design of software interface.

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Modeling and Animating Virtual Humans for Real-Time Applications

International Journal of Virtual Reality ◽

10.20870/ijvr.2007.6.4.2704 ◽

2007 ◽

Vol 6 (4) ◽

pp. 11-20 ◽

Cited By ~ 13

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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