scholarly journals A Specific Algorithm Based on Motion Direction Prediction

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhesen Chu ◽  
Min Li
Keyword(s):  
Human Body ◽  
Body Position ◽  
Motion Vector ◽  
Human Motion ◽  
Generative Model ◽  
Experimental Results ◽  
Body Motion ◽  
Motion Vectors ◽  
Motion Direction ◽  
Current Frame

In this paper, we study the estimation of motion direction prediction for fast motion and propose a threshold-based human target detection algorithm using motion vectors and other data as human target feature information. The motion vectors are partitioned into regions by normalization to form a motion vector field, which is then preprocessed, and then the human body target is detected through its motion vector region block-temporal correlation to detect the human body motion target. The experimental results show that the algorithm is effective in detecting human motion targets in videos with the camera relatively stationary. The algorithm predicts the human body position in the reference frame of the current frame in the video by forward mapping the motion vector of the current frame, then uses the motion vector direction angle histogram as a matching feature, and combines it with a region matching strategy to track the human body target in the predicted region, thus realizing the human body target tracking effect. The algorithm is experimentally proven to effectively track human motion targets in videos with relatively static backgrounds. To address the problem of sample diversity and lack of quantity in a multitarget tracking environment, a generative model based on the conditional variational self-encoder conditional generation of adversarial networks is proposed, and the performance of the generative model is verified using pedestrian reidentification and other datasets, and the experimental results show that the method can take advantage of the advantages of both models to improve the quality of the generated results.

scholarly journals Sonifying the Shape of Human Body Motion using Motiongrams

2013 ◽  
Vol 8 (2) ◽  
pp. 73 ◽  
Author(s):  
Alexander Refsum Jensenius ◽  
Rolf Inge Godøy
Keyword(s):  
Human Body ◽  
Video Recording ◽  
Visual Display ◽  
Human Motion ◽  
Visual Similarity ◽  
Body Motion ◽  
Auditory Displays ◽  
Musical Sound ◽  
Starting Point ◽  
Spatial Shape

<p class="author">The paper presents sonomotiongram, a technique for the creation of auditory displays of human body motion based on motiongrams. A motiongram is a visual display of motion, based on frame differencing and reduction of a regular video recording. The resultant motiongram shows the spatial shape of the motion as it unfolds in time, somewhat similar to the way in which spectrograms visualise the shape of (musical) sound. The visual similarity of motiongrams and spectrograms is the conceptual starting point for the sonomotiongram technique, which explores how motiongrams can be turned into sound using &ldquo;inverse FFT&rdquo;. The paper presents the idea of shape-sonification, gives an overview of the sonomotiongram technique, and discusses sonification examples of both simple and complex human motion.</p>

Computer Simulation of Lower Extremities Movement during Stair Climbing

2013 ◽  
Vol 330 ◽  
pp. 407-411 ◽  
Author(s):  
Vesna Raspudić
Keyword(s):  
Human Body ◽  
Human Locomotion ◽  
Great Difficulty ◽  
Human Motion ◽  
Body Motion ◽  
Metabolic Energy ◽  
Stair Climbing ◽  
Interactive Methods ◽  
Limb Amputation ◽  
Magnetic Markers

Tracking of human body motion is applied in many fields, such as virtual reality, clinical biomechanics, the study of man-machine-environment relationship, the analysis of sports movements, etc. Nowadays, the preferred approach to tracking human body motion is based on the use of appropriate optical or magnetic markers, which are placed on specific landmark points, and real-time estimating of their spatial coordinates. With the improvements introduced in computerized monitoring of human motion kinematics, it is important to emphasize the significance of combining motion capture data with commercial CAD packages. The aim of this research was to develop new interactive methods in creating virtual models within the highly sophisticated CAD computer technologies, as well as computer simulations for analyzing the various forms of human locomotion. Within this research, special attention is focused on the study of locomotion when climbing stairs, as an activity that requires large amount of metabolic energy, and thus represents great difficulty in performing daily activities for people with disorders of the musculoskeletal system, and particularly for people with lower limb amputation.

LOG-TRACKER: AN ATTRIBUTE-BASED APPROACH TO TRACKING HUMAN BODY MOTION

1991 ◽  
Vol 05 (03) ◽  
pp. 439-458 ◽  
Author(s):  
WARREN LONG ◽  
YEE-HONG YANG
Keyword(s):  
Human Body ◽  
Moving Objects ◽  
Human Motion ◽  
The Body ◽  
Body Motion ◽  
Body Parts ◽  
Short Period ◽  
Recognition Of Objects ◽  
Analysis System ◽  
Key Steps

Motion provides extra information that can aid in the recognition of objects. One of the most commonly seen objects is, perhaps, the human body. Yet little attention has been paid to the analysis of human motion. One of the key steps required for a successful motion analysis system is the ability to track moving objects. In this paper, we describe a new system called Log-Tracker, which was recently developed for tracking the motion of the different parts of the human body. Occlusion of body parts is termed a forking condition. Two classes of forks as well as the attributes required to classify them are described. Experimental results from two gymnastics sequences indicate that the system is able to track the body parts even when they are occluded for a short period of time. Occlusions that extend for a long period of time still pose problems to Log-Tracker.

Video Steganographic Algorithm Based on Motion Vector

2014 ◽  
Vol 926-930 ◽  
pp. 3330-3333
Author(s):  
Ying Nan Zhang ◽  
Min Qing Zhang ◽  
Ke Niu
Keyword(s):  
Phase Angle ◽  
Motion Vector ◽  
Horizontal Component ◽  
Statistical Properties ◽  
Experimental Results ◽  
Secret Message ◽  
Pseudorandom Sequence ◽  
Motion Vectors ◽  
Computation Cost

Focusing on video steganographic, a method based on motion vector is proposed to solve the problem of low security and low quality of video after embedding. A set of the motion vectors with magnitude greater than a predefined threshold are selected. And the secret message is embedded in the vertical or horizontal component of those motion vectors through the proper range of the phase angle. Steganographic between the message and vector motion with pseudorandom sequence generated by key. Experimental results show that this method has low impact on cover video, and also has low computation cost, high anti-statistical properties based on motion vectors difference.

Acceleration Factor Modeling of Flexible Electronic Substrates From Actual Human Body Measurements

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Vikas Yadav ◽  
Jinesh Narangaparambil ◽  
Wei Liu
Keyword(s):  
Human Body ◽  
Flexible Electronics ◽  
Human Motion ◽  
Acceleration Factor ◽  
Body Motion ◽  
Motion Data ◽  
Factor Modeling ◽  
Electronic Substrates ◽  
Resistance Data ◽  
Full Body

Abstract The use of flexible electronics wearable applications has prompted the need to understand the stresses imposed during human motion for a range of activities. Wearable applications may involve situations in which the electronics may be flexed-to-install, stretched or subjected to thousands cycles of dynamic flexing. In order to develop meaningful test-levels, a better understanding is needed of the use-cases, variance, and the acceleration factors. In this study, the human body motion data for walking, jumping, squats, lunges, and bicep curls were measured using a set of ten Vicon cameras to measure the position, velocity, and accelerations of a standard full-body sensor location of the human body. In addition, reliability data has been gathered on test vehicles subjected to dynamic flexing. Continuous resistance data have been gathered on circuits subjected to dynamic flexing till failure for some of the commonly used trace geometries in electronic circuits. Experimental measurements during the accelerated tests of the boards were combined with the human body motion data to model the acceleration factor for different human activities based on the flexing angles. Human motion for multiple subjects and multiple joints has been correlated to the test levels for the development of acceleration factors. Statistical analysis on the variation of the joint angles with hypothesis testing has been conducted for different subjects and for different human body actions. Acceleration factors models have been developed for walking, jumping, squats, lunges, and bicep curls.

Absorption of ambiguous human motion on Human Body Motion Interface

2011 ◽  
Author(s):  
Sho Yokota ◽  
Hiroshi Hashimoto ◽  
Daisuke Chugo ◽  
Yasuhiro Ohyama ◽  
Jinhua She
Keyword(s):  
Human Body ◽  
Human Motion ◽  
Body Motion

scholarly journals Volleyball Action Extraction and Dynamic Recognition Based on Gait Tactile Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Limin Qi
Keyword(s):  
Human Body ◽  
Difference Method ◽  
Body Position ◽  
Capture Rate ◽  
Detection Algorithm ◽  
Human Motion ◽  
Position Estimation ◽  
Test Method ◽  
Frame Difference ◽  
Background Segmentation

At present, the industry research of volleyball technology is relatively in-depth, and the analysis of the muscle strength characteristics and coordination of the jumping ball is less, which is not conducive to the control of technical movements. This study used a wireless portable surface EMG tester (16 lines) to analyze the EMG of the main muscle groups in athletes’ volleyball and conducted a video synchronization test method to find the position of the human body. Therefore, a background-based frame difference method is proposed to detect the position and obtain the precise position of the human body. Experiments show that the background-based three-frame difference method effectively eliminates the “hole” effect of the original three-frame difference method and provides an accurate and complete framework for identifying the human body. Adjust the recognition frame according to the proportion of the human body in the image, and use the predefined parameters of the severe frame to perform forward/volleyball background segmentation. The novelty of this document lies in the completion of the complete human body placement of the above three tasks, precapture/background segmentation, and an improved human body position estimation algorithm to extract the human body pose from the video. First, locate the human body in each frame of the video, and then, perform the process of estimating the position of the graphic model based on the color and texture of the unit. After recognizing the gesture of each image in the video, the recognition result will be displayed. Experiments show that after detecting the position of the human body, the predefined frame setting process of the tomb is carried out in two steps, which improves the automation of the human body image detection algorithm, effectively extracts the human motion video, and increases the motion capture rate by more than 30%, to provide a useful reference for the improvement of college volleyball players’ movement skills and training competitions.

scholarly journals A Combined Softening and Hardening Mechanism for Low Frequency Human Motion Energy Harvesting Application

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Khalis Suhaimi ◽  
Roszaidi Ramlan ◽  
Azma Putra
Keyword(s):  
Human Body ◽  
Low Frequency ◽  
Vibration Signal ◽  
Human Motion ◽  
Body Motion ◽  
Measured Signal ◽  
Hardening Mechanism ◽  
Softening Mechanism ◽  
The Right ◽  
Low Amplitude

This paper concerns the mechanism for harvesting energy from human body motion. The vibration signal from human body motion during walking and jogging was first measured using 3-axes vibration recorder placed at various places on the human body. The measured signal was then processed using Fourier series to investigate its frequency content. A mechanism was proposed to harvest the energy from the low frequency-low amplitude human motion. This mechanism consists of the combined nonlinear hardening and softening mechanism which was aimed at widening the bandwidth as well as amplifying the low human motion frequency. This was realized by using a translation-to-rotary mechanism which converts the translation motion of the human motion into the rotational motion. The nonlinearity in the system was realized by introducing a winding spring stiffness and the magnetic stiffness. Quasi-static and dynamic measurement were conducted to investigate the performance of the mechanism. The results show that, with the right degree of nonlinearity, the two modes can be combined together to produce a wide flat response. For the frequency amplification, the mechanism manages to increase the frequency by around 8 times in terms of rotational speed.

An electromagnetic energy harvester using ferrofluid as a lubricant

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840084
Author(s):  
Siqi Wang ◽  
Yongkai Liu ◽  
Decai Li ◽  
Hujun Wang
Keyword(s):  
Permanent Magnet ◽  
Human Body ◽  
Permanent Magnets ◽  
Average Power ◽  
Low Frequency ◽  
Human Motion ◽  
Body Motion ◽  
Plastic Tube ◽  
Magnetic Spring ◽  
Electromagnetic Generator

An electromagnetic generator with magnetic spring and ferrofluid is proposed and designed to harvest low-frequency vibration energy from human body motion. The magnetic spring is formed by gravity and magnetic repulsive force between the fixed and the moving permanent magnets. The ferrofluid is aggregated at both ends of the moving permanent magnet, and the ferrofluid layer between the plastic tube wall and permanent magnet can remove the solid-friction as a fluid lubricant. The electromagnetic generator is used to harvest human motion energy. The measured average load powers of electromagnetic generator with ferrofluid 1.5 g from human body motion are 1.3 mW and 7.5 mW during walking and low running, respectively, which is 30 times more than the measured average power of generator without ferrofluid.

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