scholarly journals Joint Center Estimation Using Single-Frame Optimization: Part 2: Experimentation

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2563 ◽  
Author(s):  
Eric Frick ◽  
Salam Rahmatalla
Keyword(s):  
Motion Capture ◽  
Degrees Of Freedom ◽  
Inertial Sensor ◽  
Human Motion ◽  
Measurement Unit ◽  
Time Varying ◽  
Human Motion Capture ◽  
Single Frame ◽  
Joint Center ◽  
Optical Markers

Human motion capture is driven by joint center location estimates, and error in their estimation can be compounded by subsequent kinematic calculations. Soft tissue artifact (STA), the motion of tissue relative to the underlying bones, is a primary cause of error in joint center calculations. A method for mitigating the effects of STA, single-frame optimization (SFO), was introduced and numerically verified in Part 1 of this work, and the purpose of this article (Part 2) is to experimentally compare the results of SFO with a marker-based solution. The experimentation herein employed a single-degree-of-freedom pendulum to simulate human joint motion, and the effects of STA were simulated by affixing the inertial measurement unit to the pendulum indirectly through raw, vacuum-sealed meat. The inertial sensor was outfitted with an optical marker adapter so that its location could be optically determined by a camera-based motion-capture system. During the motion, inertial effects and non-rigid attachment of the inertial sensor caused the simulated STA to manifest via unrestricted motion (six degrees of freedom) relative to the rigid pendulum. The redundant inertial and optical instrumentation allowed a time-varying joint center solution to be determined both by optical markers and by SFO, allowing for comparison. The experimental results suggest that SFO can achieve accuracy comparable to that of state-of-the-art joint center determination methods that use optical skin markers (root mean square error of 7.87–37.86 mm), and that the time variances of the SFO solutions are correlated (r =  0.58–0.99) with the true, time-varying joint center solutions. This suggests that SFO could potentially help to fill a gap in the existing literature by improving the characterization and mitigation of STA in human motion capture.

CONTROLLING A MOTORIZED MARIONETTE WITH HUMAN MOTION CAPTURE DATA

2004 ◽  
Vol 01 (04) ◽  
pp. 651-669 ◽  
Author(s):  
KATSU YAMANE ◽  
JESSICA K. HODGINS ◽  
H. BENJAMIN BROWN
Keyword(s):  
Motion Capture ◽  
Degrees Of Freedom ◽  
Dynamic Properties ◽  
Human Motion ◽  
Data Sets ◽  
Motion Capture Data ◽  
Sensors And Actuators ◽  
Human Motion Capture ◽  
Motion Data ◽  
Human Actors

In this paper, we present a method for controlling a motorized, string-driven marionette using motion capture data from human actors and from a traditional marionette operated by a professional puppeteer. We are interested in using motion capture data of a human actor to control the motorized marionette as a way of easily creating new performances. We use data from the hand-operated marionette both as a way of assessing the performance of the motorized marionette and to explore whether this technology could be used to preserve marionette performances. The human motion data must be extensively adapted for the marionette because its kinematic and dynamic properties differ from those of the human actor in degrees of freedom, limb length, workspace, mass distribution, sensors, and actuators. The motion from the hand-operated marionette requires less adaptation because the controls and dynamics are a closer match. Both data sets are adapted using an inverse kinematics algorithm that takes into account marker positions, joint motion ranges, string constraints, and potential energy. We also apply a feedforward controller to prevent extraneous swings of the hands. Experimental results show that our approach enables the marionette to perform motions that are qualitatively similar to the original human motion capture data.

scholarly journals Human motion capture for movement limitation analysis using an RGB-D camera in spondyloarthritis: a validation study

2021 ◽  
Author(s):  
Manuel Trinidad-Fernández ◽  
Antonio Cuesta-Vargas ◽  
Peter Vaes ◽  
David Beckwée ◽  
Francisco-Ángel Moreno ◽  
...  
Keyword(s):  
Motion Capture ◽  
Angular Displacement ◽  
Cross Sectional Study ◽  
Human Motion ◽  
Measurement Unit ◽  
Motion Capture System ◽  
Validity And Reliability ◽  
Timed Up And Go ◽  
Cross Sectional ◽  
Human Motion Capture

AbstractA human motion capture system using an RGB-D camera could be a good option to understand the trunk limitations in spondyloarthritis. The aim of this study is to validate a human motion capture system using an RGB-D camera to analyse trunk movement limitations in spondyloarthritis patients. Cross-sectional study was performed where spondyloarthritis patients were diagnosed with a rheumatologist. The RGB-D camera analysed the kinematics of each participant during seven functional tasks based on rheumatologic assessment. The OpenNI2 library collected the depth data, the NiTE2 middleware detected a virtual skeleton and the MRPT library recorded the trunk positions. The gold standard was registered using an inertial measurement unit. The outcome variables were angular displacement, angular velocity and lineal acceleration of the trunk. Criterion validity and the reliability were calculated. Seventeen subjects (54.35 (11.75) years) were measured. The Bending task obtained moderate results in validity (r = 0.55–0.62) and successful results in reliability (ICC = 0.80–0.88) and validity and reliability of angular kinematic results in Chair task were moderate and (r = 0.60–0.74, ICC = 0.61–0.72). The kinematic results in Timed Up and Go test were less consistent. The RGB-D camera was documented to be a reliable tool to assess the movement limitations in spondyloarthritis depending on the functional tasks: Bending task. Chair task needs further research and the TUG analysis was not validated. Graphical abstract Comparation of both systems, required software for camera analysis, outcomes and final results of validity and reliability of each test.

A Human Motion Capture System Based on Inertial Sensing and a Complementary Filter

2013 ◽  
Author(s):  
Kan Kanjanapas ◽  
Yizhou Wang ◽  
Wenlong Zhang ◽  
Lauren Whittingham ◽  
Masayoshi Tomizuka
Keyword(s):  
Motion Capture ◽  
Human Motion ◽  
Central Processing Unit ◽  
Measurement Unit ◽  
Processing Unit ◽  
Motion Capture System ◽  
Inertial Sensing ◽  
Human Motion Capture ◽  
Central Processing ◽  
Complementary Filter

A human motion capture system is becoming one of the most useful tools in rehabilitation application because it can record and reconstruct a patient’s motion accurately for motion analysis. In this paper, a human motion capture system is proposed based on inertial sensing. A microprocessor is implemented on-board to obtain raw sensing data from the inertial measurement unit (IMU), and transmit the raw data to the central processing unit. To reject noise in the accelerometer, drift in the gyroscope, and magnetic distortion in the magnetometer, a time-varying complementary filter (TVCF) is implemented in the central processing unit to provide accurate attitude estimation. A forward kinematic model of the human arm is developed to create an animation for patients and physical therapists. Performance of the hardware and filtering algorithm is verified by experimental results.

Real-Time Human Motion Capture Based on Wearable Inertial Sensor Networks

2021 ◽  
pp. 1-1
Author(s):  
Jie Li ◽  
Xiao Feng Liu ◽  
Zhelong Wang ◽  
Hongyu Zhao ◽  
Tingting Zhang ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Motion Capture ◽  
Inertial Sensor ◽  
Human Motion ◽  
Human Motion Capture

Keyframe Extraction for Human Motion Capture Data Based on Joint Kernel Sparse Representation

2017 ◽  
Vol 64 (2) ◽  
pp. 1589-1599 ◽  
Author(s):  
Guiyu Xia ◽  
Huaijiang Sun ◽  
Xiaoqing Niu ◽  
Guoqing Zhang ◽  
Lei Feng
Keyword(s):  
Sparse Representation ◽  
Motion Capture ◽  
Human Motion ◽  
Motion Capture Data ◽  
Human Motion Capture ◽  
Keyframe Extraction

Sensor network oriented human motion capture via wearable intelligent system

2021 ◽  
Author(s):  
Sen Qiu ◽  
Hongkai Zhao ◽  
Nan Jiang ◽  
Donghui Wu ◽  
Guangcai Song ◽  
...  
Keyword(s):  
Sensor Network ◽  
Motion Capture ◽  
Intelligent System ◽  
Human Motion ◽  
Human Motion Capture
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