scholarly journals Capture of 3D Human Motion Pose in Virtual Reality Based on Video Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Qiang Fu ◽  
Xingui Zhang ◽  
Jinxiu Xu ◽  
Haimin Zhang
Keyword(s):  
Virtual Reality ◽  
Fuzzy Logic ◽  
Human Body ◽  
Estimation Algorithm ◽  
Human Motion ◽  
Body Motion ◽  
Classification Model ◽  
Accuracy Rate ◽  
Motion State ◽  
Adaptive Adjustment

Motion pose capture technology can effectively solve the problem of difficulty in defining character motion in the process of 3D animation production and greatly reduce the workload of character motion control, thereby improving the efficiency of animation development and the fidelity of character motion. Motion gesture capture technology is widely used in virtual reality systems, virtual training grounds, and real-time tracking of the motion trajectories of general objects. This paper proposes an attitude estimation algorithm adapted to be embedded. The previous centralized Kalman filter is divided into two-step Kalman filtering. According to the different characteristics of the sensors, they are processed separately to isolate the cross-influence between sensors. An adaptive adjustment method based on fuzzy logic is proposed. The acceleration, angular velocity, and geomagnetic field strength of the environment are used as the input of fuzzy logic to judge the motion state of the carrier and then adjust the covariance matrix of the filter. The adaptive adjustment of the sensor is converted to the recognition of the motion state. For the study of human motion posture capture, this paper designs a verification experiment based on the existing robotic arm in the laboratory. The experiment shows that the studied motion posture capture method has better performance. The human body motion gesture is designed for capturing experiments, and the capture results show that the obtained pose angle information can better restore the human body motion. A visual model of human motion posture capture was established, and after comparing and analyzing with the real situation, it was found that the simulation approach reproduced the motion process of human motion well. For the research of human motion recognition, this paper designs a two-classification model and human daily behaviors for experiments. Experiments show that the accuracy of the two-category human motion gesture capture and recognition has achieved good results. The experimental effect of SVC on the recognition of two classifications is excellent. In the case of using all optimization algorithms, the accuracy rate is higher than 90%, and the final recognition accuracy rate is also higher than 90%. In terms of recognition time, the time required for human motion gesture capture and recognition is less than 2 s.

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.

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 Human Motion State Recognition Based on Flexible, Wearable Capacitive Pressure Sensors

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1219
Author(s):  
Qingyang Yu ◽  
Peng Zhang ◽  
Yucheng Chen
Keyword(s):  
Flexible Substrate ◽  
Recognition Rate ◽  
Pressure Sensors ◽  
Recognition System ◽  
Human Motion ◽  
Body Motion ◽  
Capacitive Pressure Sensor ◽  
Wearable Sensor ◽  
State Recognition ◽  
Motion State

Human motion state recognition technology based on flexible, wearable sensor devices has been widely applied in the fields of human–computer interaction and health monitoring. In this study, a new type of flexible capacitive pressure sensor is designed and applied to the recognition of human motion state. The electrode layers use multi-walled carbon nanotubes (MWCNTs) as conductive materials, and polydimethylsiloxane (PDMS) with microstructures is embedded in the surface as a flexible substrate. A composite film of barium titanate (BaTiO3) with a high dielectric constant and low dielectric loss and PDMS is used as the intermediate dielectric layer. The sensor has the advantages of high sensitivity (2.39 kPa−1), wide pressure range (0–120 kPa), low pressure resolution (6.8 Pa), fast response time (16 ms), fast recovery time (8 ms), lower hysteresis, and stability. The human body motion state recognition system is designed based on a multi-layer back propagation neural network, which can collect, process, and recognize the sensor signals of different motion states (sitting, standing, walking, and running). The results indicate that the overall recognition rate of the system for the human motion state reaches 94%. This proves the feasibility of the human motion state recognition system based on the flexible wearable sensor. Furthermore, the system has high application potential in the field of wearable motion detection.

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.

scholarly journals Digitalization of Cross-Country Skiing Training Based on Multisensor Combination

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xingxing Li ◽  
Lulu Song ◽  
Hao Wu
Keyword(s):  
Information Integration ◽  
Estimation Algorithm ◽  
Human Motion ◽  
Body Motion ◽  
Displacement Estimation ◽  
The Status ◽  
Posture Prediction ◽  
Cross Country Skiing ◽  
Cross Country

The status and role of science and technology in the field of modern competitive sports have become increasingly prominent. The construction of a scientific training command system is of great significance for improving the scientific level of the training process and deepening the digital cognition of ski training. This paper is based on the multisensor combination to conduct a digital research on cross-country skiing training, aiming to conduct in-depth research on the realization of human motion capture and the theory of motion inertial sensing. To build a scientific, formal, and malleable ski training program, the requirements for data acquisition, recording, and analysis are quite strict. For this, it is necessary to use scientific and reasonable tools combined with multiple algorithms to process information and data. During the experiment, accelerometers, gyroscopes, and magnetometers are selected as sensors to receive motion information, and recognition algorithms for identifying weightlessness, hybrid filtering algorithm, displacement estimation algorithm, and kinematic principles are adapted to process multisensor data using information integration technology. A human body motion model was established based on kinematic principles, and a cross-country skiing motion measurement program was designed. The experimental results show that, according to the combination of multisensing and video platform, the athlete’s posture prediction is adjusted, and the action on the track is more consistent, which can accelerate the athlete’s skiing speed and the size of the inclination angle to a large extent. It can affect the direction of the athlete’s borrowing force and the adjustment of gravity during the exercise. The tilt angle is expanded from 135° to 170°, and it can maintain good continuity during the exercise.

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 Human Fall Detection using Accelerometer and Gyroscope Sensors in Unconstrained Smartphone Positions

2019 ◽  
Vol 8 (3) ◽  
pp. 69-75 ◽  
Keyword(s):  
Human Body ◽  
Time Series Data ◽  
Fall Detection ◽  
Decision Fusion ◽  
Human Motion ◽  
High Rate ◽  
Series Data ◽  
Accuracy Rate ◽  
Motion Features ◽  
Detector Model

This study explored several methods for detecting body falls based on the data captured by the sensors (accelerometer and gyroscope) built in a smartphone carried by a person. The data for this study were collected by recording many sample units from each of the following human activity categories: stand-fall, walk-fall, stand-jump, stand-sit, stand, and walk. Several time-series data captured by the sensors were used as human motion features. One of the challenges of this study was the existence of human body motions whose features resembled those of body falls. In addition, unfixed smartphone positioning made human body falls harder to detect and can lead to high rate of misclassification (not detected as fall). This incident can caused serious bone fracture or even death if the person not handled as immediately as possible because of misclassification. To address this problem, we modified Resultant Acceleration and ∠ Y formulas to address the problem of unconstrained smartphone positions. We proposed to combine five methods such as AGVeSR, Alim, ∠α, GyroReDi, and AGPeak to build a robust detector model to reduce the misclassification. The experiment results showed that the accuracy of the combination of both sensors (accelerometer and gyroscope) outperformed the accuracy of accelerometer only by more than 15%. The decision fusion that used voting involving five methods could boost the accuracy rate by up to 4.15%

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.

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