Synthesis of Spatial Mechanisms to Model Human Joints

2013 ◽  
pp. 49-84 ◽  
Author(s):  
Vincenzo Parenti-Castelli ◽  
Nicola Sancisi
Keyword(s):  
Spatial Mechanisms ◽  
Human Joints

Rheological and frictional analysis of viscosupplements towards improved lubrication of human joints

2021 ◽  
Vol 160 ◽  
pp. 107030
Author(s):  
David Rebenda ◽  
Martin Vrbka ◽  
David Nečas ◽  
Evgeniy Toropitsyn ◽  
Seido Yarimitsu ◽  
...  
Keyword(s):  
Human Joints

scholarly journals HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1356
Author(s):  
Linda Christin Büker ◽  
Finnja Zuber ◽  
Andreas Hein ◽  
Sebastian Fudickar
Keyword(s):  
Color Images ◽  
Depth Image ◽  
Accuracy Evaluation ◽  
Timed Up And Go ◽  
Position Errors ◽  
Depth Images ◽  
Upper And Lower Extremities ◽  
Rgb Images ◽  
Human Joints ◽  
Body Joints

With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images like robustness to light variation or color- and texture invariance. Correspondingly, we introduce High- Resolution Depth Net (HRDepthNet)—a machine learning driven approach to detect human joints (body, head, and upper and lower extremities) in purely depth images. HRDepthNet retrains the original HRNet for depth images. Therefore, a dataset is created holding depth (and RGB) images recorded with subjects conducting the timed up and go test—an established geriatric assessment. The images were manually annotated RGB images. The training and evaluation were conducted with this dataset. For accuracy evaluation, detection of body joints was evaluated via COCO’s evaluation metrics and indicated that the resulting depth image-based model achieved better results than the HRNet trained and applied on corresponding RGB images. An additional evaluation of the position errors showed a median deviation of 1.619 cm (x-axis), 2.342 cm (y-axis) and 2.4 cm (z-axis).

GPFS: A Graph-based Human Pose Forecasting System for Smart Home with Online Learning

2021 ◽  
Vol 17 (3) ◽  
pp. 1-19
Author(s):  
Xin Li ◽  
Dawei Li
Keyword(s):  
Online Learning ◽  
Smart Home ◽  
Data Privacy ◽  
Structural Information ◽  
Learning Technology ◽  
Extensive Evaluation ◽  
Forecasting System ◽  
Human Pose ◽  
Human Joints ◽  
Visual Domain

Forecasting human poses given a sequence of historical pose frames has several important applications, especially in the domain of smart home safety. Recently, computer vision-based human pose forecasting has made a breakthrough using deep learning technology. However, to implement a practical system deployed on an IoT edge environment, there are still two issues to be addressed. First, existing methods on pose forecasting fail to model the coherent structural information of connected human joints and thus cannot achieve satisfactory prediction accuracy, especially for long-term predictions. Second, a general and static pre-trained prediction model may not perform well in the deployment environment due to the visual domain shift problem. In this article, we propose a hybrid cloud-edge system called GPFS to solve those issues. Specifically, we first introduce a novel graph convolutional neural network (GCN)-based sequence-to-sequence learning method, which enhances the sequence encoder by using a graph to represent both the spatial and temporal connections of the human joints in the input frames. The GCN improves the forecasting accuracy by capturing the motion pattern of each joint as well as the correlations among different human joints over time. Second, to address the domain shift issue and protect data privacy, we extend the system to perform online learning on the IoT edge to adapt the cloud trained general model with online collected on-site domain data. Extensive evaluation on Human 3.6M and Penn Action datasets demonstrates the superiority of our proposed system.

On the shaft locations of two contra-rotating counterweights for balancing spatial mechanisms

1997 ◽  
Vol 211 (8) ◽  
pp. 567-578 ◽  
Author(s):  
S-T Chiou ◽  
J-C Tzou
Keyword(s):  
Root Mean Square ◽  
Mean Square ◽  
Spatial Mechanism ◽  
Spatial Mechanisms ◽  
Crank Mechanism ◽  
Frequency Term ◽  
Shaking Moment

It has been shown in a previous work that a frequency term of the shaking force of spatial mechanisms, whose hodograph is proved to be an ellipse, can be eliminated by a pair of contrarotating counterweights. In this work, it is found that the relevant frequency term of the shaking moment is minimized if the balancing shafts are coaxial at the centre of a family of ellipsoids, called isomomental ellipsoids, with respect to (w.r.t.) any point on an ellipsoid, as is also the root mean square (r.m.s.) of the relevant frequency term of the shaking moment. It can also be minimized even though the location of either shaft, but not both, is chosen arbitrarily on a plane. The location of the second shaft is then determinate. In order to locate the centre, a derivation for the theory of isomomental ellipsoids of a frequency term of the shaking moment of spatial mechanisms is given. It is shown that the r.m.s. of a frequency term shaking moment of a spatial mechanism w.r.t. the concentric centre of the isomomental ellipsoids is the minimum. Examples of a seven-link 7-R spatial linkage and a spatial slider-crank mechanism are included.

Study of the effect of mechanical working on the rheological properties of some potential artificial lubricants for human joints

1971 ◽  
Vol 10 (1) ◽  
pp. 21-27 ◽  
Author(s):  
M. A. M. A. Younes ◽  
P. S. Walker ◽  
P. C. Seller ◽  
D. Dowson ◽  
V. Wright
Keyword(s):  
Rheological Properties ◽  
Mechanical Working ◽  
Human Joints

The two-rotating-mass balancers for partial balancing of spatial mechanisms

1997 ◽  
Vol 32 (5) ◽  
pp. 617-628 ◽  
Author(s):  
S.-T. Chiou ◽  
M.-G. Shieh ◽  
R.-J. Tsai
Keyword(s):  
Spatial Mechanisms

scholarly journals Gene transfer to human joints: Progress toward a gene therapy of arthritis

2005 ◽  
Vol 102 (24) ◽  
pp. 8698-8703 ◽  
Author(s):  
C. H. Evans ◽  
P. D. Robbins ◽  
S. C. Ghivizzani ◽  
M. C. Wasko ◽  
M. M. Tomaino ◽  
...  
Keyword(s):  
Gene Therapy ◽  
Gene Transfer ◽  
Human Joints
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