scholarly journals A Two-Stage Fall Recognition Algorithm Based on Human Posture Features

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6966
Author(s):  
Kun Han ◽  
Qiongqian Yang ◽  
Zefan Huang
Keyword(s):  
Human Body ◽  
Stable State ◽  
Recognition Algorithm ◽  
The Body ◽  
Support Vector ◽  
Unstable State ◽  
Detection Accuracy ◽  
Two Stage ◽  
Human Posture ◽  
Key Features

Falls are seriously threatening the health of elderly. In order to reduce the potential danger caused by falls, this paper proposes a two-stage fall recognition algorithm based on human posture features. For preprocessing, we construct the new key features: deflection angles and spine ratio to describe the changes of human posture based on the human skeleton extracted by OpenPose. In the first stage, based on the variables: tendency symbol and steady symbol integrated by the scattered key features, we divide the human body state into three states: stable state, fluctuating state, and disordered state. By analyzing whether the body is in a stable state, the ADL (activities of daily living) actions with high stability can be preliminarily excluded. In the second stage: to further identify the confusing ADL actions and the fall actions, we innovatively design a time-continuous recognition algorithm. When human body is constantly in an unstable state, the human posture features: compare value γ, energy value ε, state score τ are proposed to form a feature vector, and support vector machine (SVM), K nearest neighbors (KNN), decision tree (DT), random forest (RF) are utilized for classification. Experiment results demonstrate that SVM with linear kernel function can distinguish falling actions best and our approach achieved a detection accuracy of 97.34%, precision of 98.50%, and the recall, F1 score are 97.33%, 97.91% respectively. Compared with previous state-of-art algorithms, our algorithm can achieve the highest recognition accuracy. It proves that our fall detection method is effective.

scholarly journals Human body-fluid proteome: quantitative profiling and computational prediction

2020 ◽  
Author(s):  
Lan Huang ◽  
Dan Shao ◽  
Yan Wang ◽  
Xueteng Cui ◽  
Yufei Li ◽  
...  
Keyword(s):  
High Throughput ◽  
Human Body ◽  
Body Fluid ◽  
Biomarker Discovery ◽  
Interaction Network ◽  
Computational Prediction ◽  
Computational Effort ◽  
The Body ◽  
Support Vector ◽  
Learning Approaches

Abstract Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein–protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

Human Detection Based on SVM and Improved Histogram of Oriented Gradients

2013 ◽  
Vol 380-384 ◽  
pp. 3862-3865 ◽  
Author(s):  
Li Hong Zhang
Keyword(s):  
Support Vector Machine ◽  
Linear Function ◽  
Detection System ◽  
Features Extraction ◽  
The Body ◽  
Human Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Histogram Of Oriented Gradients ◽  
Feature Description

Considering the fact that original histogram of oriented gradients (HOG) cannot extract the body local features in large image regions, its features are improved when extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Combining with HOG extraction and SVM training, the process includes three steps: features extraction, training and detection. Experiments show that while maintaining a relatively satisfactory speed the human detection system improves detection accuracy.

scholarly journals Human Body 3D Posture Estimation Using Significant Points and Two Cameras

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Chia-Feng Juang ◽  
Teng-Chang Chen ◽  
Wei-Chin Du
Keyword(s):  
Human Body ◽  
Color Space ◽  
Three Dimensional ◽  
The Body ◽  
Support Vector ◽  
Segmentation Method ◽  
Current Frame ◽  
Depth Sensors ◽  
Posture Estimation ◽  
Estimation System

This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures.

scholarly journals A Two-Stage Cascaded Detection Scheme for Double HEVC Compression Based on Temporal Inconsistency

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Peisong He ◽  
Hongxia Wang ◽  
Ruimei Zhang ◽  
Yue Li
Keyword(s):  
Video Clip ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Bit Rate ◽  
Two Stage ◽  
Detection Scheme ◽  
Double Compression ◽  
Leibler Divergence ◽  
Temporal Inconsistency

Nowadays, verifying the integrity of digital videos is significant especially for applications about multimedia communication. In video forensics, detection of double compression can be treated as the first step to analyze whether a suspicious video undergoes any tampering operations. In the last decade, numerous detection methods have been proposed to address this issue, but most existing methods design a universal detector which is hard to handle various recompression settings efficiently. In this work, we found that the statistics of different Coding Unit (CU) types have dissimilar properties when original videos are recompressed by the increased and decreased bit rates. It motivates us to propose a two-stage cascaded detection scheme for double HEVC compression based on temporal inconsistency to overcome limitations of existing methods. For a given video, CU information maps are extracted from each short-time video clip using our proposed value mapping strategy. In the first detection stage, a compact feature is extracted based on the distribution of different CU types and Kullback–Leibler divergence between temporally adjacent frames. This detection feature is fed into the Support Vector Machine classifier to identify abnormal frames with the increased bit rate. In the second stage, a shallow convolutional neural network equipped with dense connections is designed carefully to learn robust spatiotemporal representations, which can identify abnormal frames with the decreased bit rate whose forensic traces are less detectable. In experiments, the proposed method can achieve more promising detection accuracy compared with several state-of-the-art methods under various coding parameter settings, especially when the original video is recompressed with a low quality (e.g., more than 8%).

scholarly journals Human Fall Detection Based on Body Posture Spatio-Temporal Evolution

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 946 ◽  
Author(s):  
Jin Zhang ◽  
Cheng Wu ◽  
Yiming Wang
Keyword(s):  
Time Series ◽  
Inverted Pendulum ◽  
Temporal Evolution ◽  
Fall Detection ◽  
Unstable State ◽  
Detection Accuracy ◽  
Human Posture ◽  
Inverted Pendulum Model ◽  
Multi Stage ◽  
Spatio Temporal

Abnormal falls in public places have significant safety hazards and can easily lead to serious consequences, such as trampling by people. Vision-driven fall event detection has the huge advantage of being non-invasive. However, in actual scenes, the fall behavior is rich in diversity, resulting in strong instability in detection. Based on the study of the stability of human body dynamics, the article proposes a new model of human posture representation of fall behavior, called the “five-point inverted pendulum model”, and uses an improved two-branch multi-stage convolutional neural network (M-CNN) to extract and construct the inverted pendulum structure of human posture in real-world complex scenes. Furthermore, we consider the continuity of the fall event in time series, use multimedia analytics to observe the time series changes of human inverted pendulum structure, and construct a spatio-temporal evolution map of human posture movement. Finally, based on the integrated results of computer vision and multimedia analytics, we reveal the visual characteristics of the spatio-temporal evolution of human posture under the potentially unstable state, and explore two key features of human fall behavior: motion rotational energy and generalized force of motion. The experimental results in actual scenes show that the method has strong robustness, wide universality, and high detection accuracy.

Correction of Posture Deviations in the Sagittal Plane Using the Pilates Method

2019 ◽  
pp. 3-13
Author(s):  
Alexandru Cîtea ◽  
George-Sebastian Iacob
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Postural Control ◽  
Human Body ◽  
Sagittal Plane ◽  
The Body ◽  
Lower Limbs ◽  
Low Back ◽  
Pilates Method ◽  
The Relationship

Posture is commonly perceived as the relationship between the segments of the human body upright. Certain parts of the body such as the cephalic extremity, neck, torso, upper and lower limbs are involved in the final posture of the body. Musculoskeletal instabilities and reduced postural control lead to the installation of nonstructural posture deviations in all 3 anatomical planes. When we talk about the sagittal plane, it was concluded that there are 4 main types of posture deviation: hyperlordotic posture, kyphotic posture, rectitude and "sway-back" posture.Pilates method has become in the last decade a much more popular formof exercise used in rehabilitation. The Pilates method is frequently prescribed to people with low back pain due to their orientation on the stabilizing muscles of the pelvis. Pilates exercise is thus theorized to help reactivate the muscles and, by doingso, increases lumbar support, reduces pain, and improves body alignment.

Representation of a Human Body: A Comparison Study between Balinese and Javanese Traditional House

Humaniora ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 83-90
Author(s):  
Anak Agung Ayu Wulandari ◽  
Ade Ariyani Sari Fajarwati
Keyword(s):  
Comparative Study ◽  
Human Body ◽  
Qualitative Method ◽  
Descriptive Analysis ◽  
The Body ◽  
Comparison Study ◽  
Architectural Drawing ◽  
Study Approach ◽  
The Comparative Study ◽  
Head Area

The research would look further at the representation of the human body in both Balinese and Javanese traditional houses and compared the function and meaning of each part. To achieve the research aim, which was to evaluate and compare the representation of the human body in Javanese and Balinese traditional houses, a qualitative method through literature and descriptive analysis study was conducted. A comparative study approach would be used with an in-depth comparative study. It would revealed not only the similarities but also the differences between both subjects. The research shows that both traditional houses represent the human body in their way. From the architectural drawing top to bottom, both houses show the same structure that is identical to the human body; head at the top, followed by the body, and feet at the bottom. However, the comparative study shows that each area represents a different meaning. The circulation of the house is also different, while the Balinese house is started with feet and continued to body and head area. Simultaneously, the Javanese house is started with the head, then continued to body, and feet area.

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