scholarly journals Human Motion Recognition of Knitted Flexible Sensor in Walking Cycle

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 35 ◽  
Author(s):  
Yutian Li ◽  
Xuhong Miao ◽  
Li Niu ◽  
Gaoming Jiang ◽  
Pibo Ma
Keyword(s):  
Human Body ◽  
Large Strain ◽  
Human Motion ◽  
Electrical Signal ◽  
Electrical Performance ◽  
Resistance Characteristic ◽  
Body Movements ◽  
The Status ◽  
Human Motion Recognition ◽  
Knitted Structure

Knitted fabric sensors have been widely used as strain sensors in the sports health field and its large strain performance and structure are suitable for human body movements. When a knitted structure is worn, different human body movements are reflected through the large strain deformation of fabric structure and consequently change the electrical signal. Here, the mechanical and electrical properties of highly elastic knitted sweatpants were tested under large strain. This sensor has good sensitivity and stability during movement. Compared with traditional motion monitoring, this technique divides the walking cycle into two stages, namely, stance and swing phases, which can be further subdivided into six stages. The corresponding resistance characteristic values can accurately distinguish the gait cycle. Analysis on hysteresis and repeatability revealed that the sensor exhibits a constant electrical performance. Four kinds of motion postures were predicted and judged by comparing the resistance characteristic range value, peak value calculation function and time axis. The measured sensor outputs were transferred to a computer via 4.0 Bluetooth. Matlab language was used to detect the status through a rule-based algorithm and the sensor outputs.

scholarly journals A Conceptual AI-System Model for Home Automation and Smart Monitoring Based On Vision

2019 ◽  
Vol 8 (10) ◽  
pp. 3099-3102
Keyword(s):  
Associate Degree ◽  
Serial Communication ◽  
Human Motion ◽  
System Model ◽  
Home Automation ◽  
The Status ◽  
Human Motion Recognition ◽  
Acquisition Device ◽  
Future Direction ◽  
The Ideal

Home automation is a rapidly increasing developmental area in the terribly present days attributable to the higher rate of affordability and ease. It provides the potential of controlling our homes and having the sides retort to events that are managed mechanically, it has become an extremely popular attribute due to its safety functions and reasons of value. We projected a model to introduce the whole automation of our house with a security system for our household. The ideal concept of this project is to ascertain an answer that can be achieved within a minimal budget. We got so used off shelf elements aspect that the amount reduced drastically. This study conjointly discusses the chosen literature, fashionable datasets, and concludes with the challenges within the domain at the side of a future direction. The main conceptual focus of this paper is to pose a system based design that demonstrates the interfacing between MATLAB with camera and Arduino board for observation and management of household instruments. In the projected system, Arduino board is interfaced with MATLAB victimization serial communication to regulate home appliances. Image acquisition device is interfaced to MATLAB which will ceaselessly show the status of the different type of household equipment on the Graphical computer program [GUI] designed in MATLAB. When the correct commands are dispatched from the MATLAB user interface, the corresponding household equipment is turned ON/OFF which are interfaced to Arduino through relay board. The system conjointly sends alert messages or signals if any abnormality is detected in the associate degree. The human motion recognition domain has been active for quite 20 years and has provided an oversized quantity of literature.

scholarly journals Performance Evaluation of Support Vector Machine Algorithm for Human Gesture Recognition

2020 ◽  
pp. 204-210
Author(s):  
Vina Ayumi
Keyword(s):  
Support Vector Machine ◽  
Performance Evaluation ◽  
Gesture Recognition ◽  
Research Area ◽  
Human Motion ◽  
Support Vector ◽  
Body Movements ◽  
Average Accuracy ◽  
Svm Algorithm ◽  
Human Motion Recognition

Research on human motion gesture recognition has been widely used for several technological devices to support monitoring of human-computer interaction, elderly people and so forth. This research area can be observed by conducting experiments for several body movements, such as hand movements, or body movements as a whole. Many methods have been used for human motion gesture recognition in previous studies. This paper attempted to collect data of performance evaluation of support vector machine algorithms for human motion recognition. We developed research methodology that is adapted PRISMA. This methodology is consisted of four main steps for reviewing scientific articles, including identification, screening, eligibility and inclusion criteria. After we obtained result of systematic literature review. We also conducted pilot study of SVM implementation for human gesture recognition. Based on the previous study result, the accuracy performance of vector machine algorithms for body gesture dataset is between 82.88% - 99.92% and hand gesture dataset 88.24% - 95.42%. Based on our pilot experiment, recognition accuracy with the SVM algorithm for human gesture recognition achieved 94,50% (average) accuracy.

scholarly journals Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zuo Wu
Keyword(s):  
Image Segmentation ◽  
Human Body ◽  
Motion Tracking ◽  
Human Motion ◽  
Depth Information ◽  
Two Dimensional ◽  
Motion Recognition ◽  
Otsu Method ◽  
Recognition Function ◽  
Human Motion Recognition

Human motion recognition has an important application value in scenarios such as intelligent monitoring and advanced human-computer interaction, and it is an important research direction in the field of computer vision. Traditional human motion recognition algorithms based on two-dimensional cameras are susceptible to changes in light intensity and texture. The advent of depth sensors, especially the Kinect series with good performance and low price released by Microsoft, enables extensive research based on depth information. However, to a large extent, the depth information has not overcome these problems based on two-dimensional images. This article introduces the research background and significance of human motion recognition technology based on depth information, introduces in detail the research methods of human motion recognition algorithms based on depth information at home and abroad, and analyzes their advantages and disadvantages. The public dataset is introduced. Then, based on the depth information, a method of human motion recognition is proposed and optimized. A moving human body image segmentation method based on an improved two-dimensional Otsu method is proposed to solve the problem of inaccurate and slow segmentation of moving human body images using the two-dimensional Otsu method. In the process of constructing the threshold recognition function, this algorithm not only uses the cohesion of the pixels within the class but also considers the maximum variance between the target class and the background class. Then, the quantum particle swarm algorithm is used to find the optimal threshold solution of the threshold recognition function. Finally, the optimal solution is used to achieve accurate and fast image segmentation, which increases the accuracy of human body motion tracking by more than 30%.

Somewhere between Human, Nonhuman, and Woman

2020 ◽  
Vol 6 (3) ◽  
pp. 21-51
Author(s):  
Debashree Mukherjee
Keyword(s):  
Object Relations ◽  
Human Body ◽  
Film Industry ◽  
The South ◽  
Hunger Strike ◽  
Cultural Events ◽  
Production Experience ◽  
Human Object ◽  
The Status ◽  
History Of

In 1939, at the height of her stardom, the actress Shanta Apte went on a spectacular hunger strike in protest against her employers at Prabhat Studios in Poona, India. The following year, Apte wrote a harsh polemic against the extractive nature of the film industry. In Jaau Mi Cinemaat? (Should I Join the Movies?, 1940), she highlighted the durational depletion of the human body that is specific to acting work. This article interrogates these two unprecedented cultural events—a strike and a book—opening them up toward a history of embodiment as production experience. It embeds Apte's emphasis on exhaustion within contemporaneous debates on female stardom, industrial fatigue, and the status of cinema as work. Reading Apte's remarkable activism as theory from the South helps us rethink the meanings of embodiment, labor, materiality, inequality, resistance, and human-object relations in cinema.

Striking Factory and Strike of Consciousness in the Work of S. M. Eisenstein

2014 ◽  
Author(s):  
Elen Vogman
Keyword(s):  
Political Economy ◽  
Soviet Union ◽  
Human Body ◽  
Body Movements ◽  
The Soviet Union ◽  
The Everyday ◽  
Everyday Work ◽  
Political Model

The Soviet Union of the 1920s produces and supports multiple connections between the policy of work in factories and the research in medical, neurological, and collective physiology. The theatrical and cinematic work of S. M. Eisenstein forms a specific prism where these interconnections appear in a spectrum of concrete attempts to engage the factory as an aesthetic and political model. The factory as a concrete topos which Eisenstein exploits in Gas Masks and Strike questions the interrelations between the human body and machine in a new iconology of a striking factory. For the duration of the Strike, the factory is represented beyond any functionality: the workers’ body movements and gestures are all the more expressive the less they have to do with their everyday work. This modulated status of production appears in Capital, Eisenstein’s unfulfilled project to realize Marx’s political economy with methods of inner monologue invented by Joyce. This last project transfigures the factory strike into the structure of cinematographic thinking where the neuro-sensorial stimuli constantly strike the logic of the everyday consciousness in the non-personal, polyphonic, and intimate monologue.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

scholarly journals Application of human motion recognition technology in extreme learning machine

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098321
Author(s):  
Anzhu Miao ◽  
Feiping Liu
Keyword(s):  
Extreme Learning Machine ◽  
Research Work ◽  
Human Motion ◽  
Feature Descriptor ◽  
Background Variable ◽  
Motion Recognition ◽  
Network Training ◽  
Other Information ◽  
Human Motion Recognition ◽  
Learning Machine

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.

scholarly journals Elastic Simulation of Joints with Particle-Based Fluid

2021 ◽  
Vol 11 (15) ◽  
pp. 6900
Author(s):  
Su-Kyung Sung ◽  
Sang-Won Han ◽  
Byeong-Seok Shin
Keyword(s):  
Principal Stress ◽  
Human Body ◽  
Yield Condition ◽  
Maximum Principal Stress ◽  
Human Motion ◽  
The Difference ◽  
The Moment ◽  
External Forces ◽  
Physical Changes ◽  
Elastic Simulation

Skinning, which is used in skeletal simulations to express the human body, has been weighted between bones to enable muscle-like motions. Weighting is not a form of calculating the pressure and density of muscle fibers in the human body. Therefore, it is not possible to express physical changes when external forces are applied. To express a similar behavior, an animator arbitrarily customizes the weight values. In this study, we apply the kernel and pressure-dependent density variations used in particle-based fluid simulations to skinning simulations. As a result, surface tension and elasticity between particles are applied to muscles, indicating realistic human motion. We also propose a tension yield condition that reflects Tresca’s yield condition, which can be easily approximated using the difference between the maximum and minimum values of the principal stress to simulate the tension limit of the muscle fiber. The density received by particles in the kernel is assumed to be the principal stress. The difference is calculated by approximating the moment of greatest force to the maximum principal stress and the moment of least force to the minimum principal stress. When the density of a particle increases beyond the yield condition, the object is no longer subjected to force. As a result, one can express realistic muscles.

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