Identification Of Human Behavior Research Articles

A method for human behavior identification based on integrated sensor data using XGBoost classifier with PCA techniques

In recent decades, there has been a noticeable increase in the recognition among professionals of the importance of human acts. The identification of human activity has gained significant prominence because of its wide-ranging applications in several domains, including healthcare and recreational activities. Moreover, a substantial body of research in the field of machine learning has been dedicated to the development of methodologies aimed at automating the identification and analysis of human behavior. This increased interest is mostly due to the fact that there are now more tools that can collect information about how people live their daily lives. The data utilized in this study is readily available for public access on the Internet. The data set under consideration comprises sensor readings from several components integrated inside the smartphone, including the global positioning system (GPS), accelerometer, magnetometer, and gyroscope. The data sets are indifferent to the categories, positions, or alignments of the items. The data set is of considerable magnitude due to its collection from several sensors, including GPS, accelerometer, magnetometer, and gyroscope. Consequently, we are employing the Principal Component Analysis (PCA) technique to diminish the dimensionality of the data and enhance its precision. Our recommendation is to utilize the XGBoost classifier in combination with Principal Component Analysis (PCA). The recommended model had a total identification rate of 97.58%. In order to demonstrate the flexibility of the proposed method, we employ a 10-fold cross-validation technique, together with the utilization of a confusion matrix and ROC curve. These evaluation metrics serve to provide a tangible illustration of the recommended strategy. The proposed methodology might have encouraging outcomes in effectively discerning human behaviors, thereby potentially facilitating the restoration of walking and pushing gaits into a Bipedal Robot and other Parkinson’s diseases.

Multi-Level Fusion for Facial Expression Recognition in Human Behavior Identification

In this study, we present a multi-level fusion of deep learning technique for facial expression identification, with applications spanning the fields of cognitive science, personality development, and the detection and diagnosis of mental health disorders in humans. The suggested approach, named Deep Learning aided Hybridized Face Expression Recognition system (DLFERS), classifies human behavior from a single image frame through the use of feature extraction and a support vector machine. An information classification algorithm is incorporated into the methodology to generate a new fused image consisting of two integrated blocks of eyes and mouth, which are very sensitive to changes in human expression and relevant for interpreting emotional expressions. The Transformation of Invariant Structural Features (TISF) and the Transformation of Invariant Powerful Movement (TIPM) are utilized to extract features in the suggested method's Storage Pack of Features (SPOF). Multiple datasets are used to compare the effectiveness of different neural network algorithms for learning facial expressions. The study's major findings show that the suggested DLFERS approach achieves an overall classification accuracy of 93.96 percent and successfully displays a user's genuine emotions during common computer-based tasks.

Human Behavior Identification Based on Graphology Using Artificial Neural Network

Handwriting reflects a person's true nature, phobias, emotional outbursts, honesty, defenses and many more characteristics. Analysis of handwriting, also known as graphology, is a science that uses the strokes and patterns disclosed by handwriting to identify, evaluate, and analyze personality. It is the study of the patterns and physical characteristics of handwriting to identify the author, indicate the author's psychological state while writing, or analyze personality traits. Traditionally, professionals also called graphologists predict the behavior of the writer by analyzing their handwriting, but this procedure is tedious and expensive. Therefore, this paper focuses on developing an application for personality identification that can predict behavioral characteristics directly using a computer without any human involvement. Most of the existing applications use English as the primary language to identify the personality trait of the writer however, our approach uses Devanagari scripts for prediction, thereby eliminating the language barrier. Our proposed method uses a machine learning approach to predict personality by analyzing Devanagari samples using Artificial Neural Network. We have created our own Devanagari word dataset. There are almost 4000 images which belong to 5 classes namely Introvert, Extrovert, Optimistic, Pessimistic and Stable mind-set. The testing accuracy achieved by the proposed method is 94.75%.

A Hybrid Deep Residual Network for Efficient Transitional Activity Recognition Based on Wearable Sensors

Numerous learning-based techniques for effective human behavior identification have emerged in recent years. These techniques focus only on fundamental human activities, excluding transitional activities due to their infrequent occurrence and short period. Nevertheless, postural transitions play a critical role in implementing a system for recognizing human activity and cannot be ignored. This study aims to present a hybrid deep residual model for transitional activity recognition utilizing signal data from wearable sensors. The developed model enhances the ResNet model with hybrid Squeeze-and-Excitation (SE) residual blocks combining a Bidirectional Gated Recurrent Unit (BiGRU) to extract deep spatio-temporal features hierarchically, and to distinguish transitional activities efficiently. To evaluate recognition performance, the experiments are conducted on two public benchmark datasets (HAPT and MobiAct v2.0). The proposed hybrid approach achieved classification accuracies of 98.03% and 98.92% for the HAPT and MobiAct v2.0 datasets, respectively. Moreover, the outcomes show that the proposed method is superior to the state-of-the-art methods in terms of overall accuracy. To analyze the improvement, we have investigated the effects of combining SE modules and BiGRUs into the deep residual network. The findings indicates that the SE module is efficient in improving transitional activity recognition.

MODFAT: Moving Object Detection by Removing Shadow Based on Fuzzy Technique with an Adaptive Thresholding Method

Nowadays, in this technology centric world, gadgets have become handy due to miniaturization. Especially cameras are widely used device for many aspects, one of the common applications is human behavior identification and intelligent video surveillance. In a such application moving object detection in complex dynamic scene is a tedious task due to various challenges such as occlusion, background illumination variation and shadow. Shadows are created in light occlusion in the object it has major impact in accurate object detection. In this paper, object detection with elimination of shadow is addressed. Many existing methods have failed in discriminating the actual moving object from shadow object very accurately. In order to overcome the limitations of existing methods, an improved fuzzy technique rule is used for shadow removal and an adaptive fuzzy thresholding is used for segmenting a foreground object in background. The proposed techniques are experimented with standard and our own datasets and also, it is compared with other existing approaches. Results of proposed method shows improved reliability.

Network of institutions, source journals, and keywords on COVID-19 by Korean authors based on the Web of Science Core Collection in January 2021

Purpose: The aim of this study was to characterize the network of institutions, journals, and topics of coronavirus disease 2019 (COVID-19) literature by Korean authors in the Web of Science Core Collection. The specific goals were to identify the collaborative relationships between Korean authors and international authors and to explore clusters of institutions, journals, and topics.Methods: Literature was searched in the Web of Science Core Collection on January 30, 2021. The search terms were “SARS-CoV-2” or “COVID” or “novel coronavirus” in the subject field. The search results were limited again to “South Korea” as the country and the publication type of “article.” The measurement tool was Biblioshiny, an app version tool for Bibliometrix.Results: Korean authors published 3.2 times more COVID-19–related articles in journals outside of Korea than in Korean journals. The journals showed three clusters by bibliographic coupling. In contrast, the co-citation network showed four clusters. Only a few journals were included in the clusters in both analyses. The conceptual structure of Keywords Plus by factorial analysis showed two clusters: “pathology and clinical treatment” and “knowledge and attitudes.” Institutions’ collaborative network consisted of four clusters. Korean researchers actively collaborated with international researchers, especially those in the United States.Conclusion: Because only a few Korean journals were included in the journal clusters by both coupling and co-citation network, more active citation of Korean journals is recommended. The identification of human behavior as a distinct theme in COVID-19 research suggests a different focus in this area besides clinical studies.

Study on Local Optical Flow Method Based on YOLOv3 in Human Behavior Recognition

In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition.

Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

The identification of human behavior can provide useful information across multiple job spectra. Recent advances in applying data-based approaches to social sciences have increased the feasibility of modeling human behavior. In particular, studying human behavior by analyzing unstructured textual data has recently received considerable attention because of the abundance of textual data. The main objective of the present study was to discuss the primary methods for identifying and predicting human behavior through the mining of unstructured textual data. Of the 823 articles analyzed, 87 met the predefined inclusion criteria and were included in the literature review. Our results show that the included articles could be symmetrically classified into two groups. The first group of articles attempted to identify the leading indicators of human behavior in unstructured textual data. In this group, the data-based approaches had three main components: (1) collecting self-reported survey data, (2) collecting data from social media and extracting data features, and (3) applying correlation analysis to evaluate the relationship between two sets of data. In contrast, the second group focused on the accuracy of data-based approaches for predicting human behavior. In this group, the data-based approaches could be categorized into (1) approaches based on labeled unstructured textual data and (2) approaches based on unlabeled unstructured textual data. The review provides a comprehensive insight into unstructured textual data mining to identify and predict human behavior and personality traits.

Research on first aid measures based on convolutional neural network recognition human actions

To explore the application of human behavior recognition based on convolutional neural network (CNN) in the new generation of pre-hospital first aid. Sixty videos were obtained from the Montreal Falling Video Data base, and divided into model training data and evaluation test data at a ratio of 5:1. (1) Data model training: singular value decomposition was used to clarify the picture, the target boundary of the human body in the picture was identified through target detection and Fourier transform, then the human body curve was described; OpenCv computer vision and machine learning software library to estimate the body pose were used to mark the important parts of the human body (such as hips, knees), the angle between the line of important parts and the horizontal direction and the length and width ratio of the detection frame were calculated, and whether the human body had abnormal behavior was identified. (2) Evaluation test: 6 videos were randomly extracted from the model training data set, 10 frame were extracted from each video, each frame was treated as one picture, CNN behavior recognition was used on each frame, and calculated the recognition rate between normal behavior and abnormal behavior. In the process of data model training, each frame was artificially labeled to train the CNN human behavior recognition model. The evaluation results showed that the recognition rate of normal behavior was (90.33±3.03)%, and the recognition rate of abnormal behavior was (87.74±2.88)%. When passers-by have dangerous behaviors, the identification of human behaviors through CNN can determine whether they are in a critical state, and issue early warning in time, which plays a vital role in pre-hospital first aid.

Video Human Behavior Recognition Based on ISA Deep Network Model

Vision-based behavior recognition is the analysis and recognition of human behavior in video. It has been widely used in many aspects such as multimedia information retrieval, behavior monitoring, and robot perception. This paper uses the Independent Subspace Analysis (ISA) deep network model feature extraction method, which is based on the ISA model and neural network theory, and combines data preprocessing methods, [Formula: see text]-means clustering methods, and Support Vector Machine (SVM) classifiers to achieve video classification and identification of human behavior. The ISA-based deep network model feature extraction method is an unsupervised learning method that can obtain behavior characteristics with good invariance and characterization capabilities in video human behavior. The experiment was conducted on the basis of the Hollywood2 human behavior data set. This experiment was compared with other commonly used human behavior feature extraction and recognition methods. The experimental results validated the effectiveness and advantages of this method in the classification and recognition of human behavior.

Optimal Control and Inverse Optimal Control with Continuous Updating for Human Behavior Modeling

The theory of optimal control has received considerable attention to model motion behavior or decision making of humans. Most approaches are based on a fixed (or infinite) time horizon which implies that all information is available at the beginning of the time interval. Nevertheless, it is reasonable to believe that the human uses information defined by a continuously moving information horizon at each time instant and adapts accordingly. Therefore, in this paper, we propose an optimal feedback control approach based on the paradigm of continuous updating. The model parameters which define individual human behavior consist of the cost function parameters and the length of the information horizon, which can be identified via a corresponding inverse optimal control approach. We show the applicability of the approach with simulations of a potential application example of human behavior identification from the point of view of a driving assistance system.

Characterizing Human Behaviours Using Statistical Motion Descriptor

Identifying human behaviors is a challenging research problem due to the complexity and variation of appearances and postures, the variation of camera settings, and view angles. In this paper, we try to address the problem of human behavior identification by introducing a novel motion descriptor based on statistical features. The method first divide the video into N number of temporal segments. Then for each segment, we compute dense optical flow, which provides instantaneous velocity information for all the pixels. We then compute Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32 bins. We then compute statistical features from the obtained HOOF forming a descriptor vector of 192- dimensions. We then train a non-linear multi-class SVM that classify different human behaviors with the accuracy of 72.1%. We evaluate our method by using publicly available human action data set. Experimental results shows that our proposed method out performs state of the art methods.

Inverse Optimal Control for Identification in Non-Cooperative Differential Games

This paper presents research on inverse optimal control in non-cooperative differential games. An inverse optimization framework for the identification of an unknown objective function of a game’s participant is introduced. The identified objective function yields the same trajectories as the ground truth objective function. We apply the presented methods in a human-machine cooperation scenario, where identification of human behavior is indispensable for automation design. The framework’s methods are evaluated through simulation of a driver cooperating with a driving assistance system based on a realistic vehicle dynamics model. The identification results are highly accurate and calculation performances motivate real-time applications.

Diagnóstico do processo de berliner, comportamento elementar e modo de erro causadores de interrupções de energia elétrica

O desempenho humano tem sido estudado em face às limitações impostas ao ambiente e que refletem no desempenho do colaborador. Para que os erros humanos sejam minimizados é necessário que sejam analisados e propostas medidas mitigatórias. O objetivo do estudo é adicionar o tipo de erro "lapso" a estrutura proposta por Vilela (2013). Posteriormente, é realizada uma análise de agrupamentos para observar quais modos de falha, comportamento elementar e processo de Berliner que causaram mais interrupções de energia elétrica entre os anos de 1998 e 2009. Para tanto, a metodologia foi baseada em uma análise de agrupamentos a qual auxiliou na divisão dos erros humanos por grupos de características semelhantes, fornecendo assim uma identificação mais realística do comportamento humano que ocasionou o erro. Por conseguinte, essa pesquisa fornece suporte para uma proposição de soluções que possam contribuir para a redução dos erros humanos. O resultado da análise de agrupamento identificou os grupos 3 e 4 como os maiores ocasionadores de interrupções de energia elétrica. Também foi realizada a mensuração dos custos envolvidos na interrupção de energia elétrica, evidenciado o alto valor do faturamento perdido pela empresa no período estudado.

Smart Homecare Surveillance System: Behavior Identification Based on State-Transition Support Vector Machines and Sound Directivity Pattern Analysis

This study presents a smart homecare surveillance system, which utilizes sound-steered cameras to identify behavior of interest. First of all, to detect multiple source locations, a new direction-of-arrival (DOA) algorithm is proposed by introducing cascaded frequency filters, which can quickly calculate directions without creating much complexity. This method can also locate and separate different signals at the same time. Second, after the camera points in the direction of the estimated angle, the proposed state-transition support vector machine is used to provide favorable discriminability for human behavior identification. A new Markov random field (MRF) function based on the localized contour sequence (LCS) is also presented while the system computes transition probabilities between states. Such LCS-based MRF functions can effectively smooth transitions and enhance recognition. The experimental results show that the average error of DOA decreases to around 7°, which is better than those of the baselines. Also, our proposed behavior identification system can reach an 88.3% accuracy rate. The aforementioned results have therefore demonstrated the feasibility of the proposed method.

Study on Based Motion in Improved Radon Transformation with Mechanics Properties

Mechanics properties of human body mechanical movement have a lot of initiative and variability, especially sports technical movement is the day after tomorrow, spontaneous or consciously form. To a video clip, mechanics properties of human motion reflect the changes which are the important characteristics of the gradual progress in video. If we want to describe the overall content of the video, the motion properties are an essential respect. An improved radon transformation method of feature extraction for human behavior identification is designed in order to solve the sensitivity of the scaling and translation invariant of conventional radon transformation. It deduces the process of the improving method to keep the scaling and translation invariant. The experimental results show that the improving method is low computation and higher recognition rate. Also, it enhances the robustness of feature extraction.