Human Pattern Recognition Research Articles

A low-cost framework for the recognition of human motion gait phases and patterns based on multi-source perception fusion

A low-cost framework for the recognition of human motion gait phases and patterns based on multi-source perception fusion

3D Dynamic Image Modeling Based on Machine Learning in Film and Television Animation

With the deep integration and rapid development of computer technology and film and television animation in recent years, computer animation technology has gradually created objective practical value. 3D animation technology also plays a key role in film and television special effects and advertising special effects. As a new type of multimedia data, human motion capture data can be used for 3D human model modeling and human motion simulation because of its high fidelity. A human motion pattern recognition method based on long short-term memory network (LSTM) is proposed. The model uses a deep learning neural network composed of a 2-layer LSTM network to automatically extract features from the collected human body feature information. Then, the multi-class motion patterns are modeled in time series to quickly identify different motion patterns in real time. To evaluate the performance of this model, the effectiveness of this method in identifying six different motion modes is validated using an open dataset. At the same time, this method is compared with four methods based on in-depth learning model. Experimental results verify the effectiveness of the method. It provides a feasible method for human motion recognition and modeling based on capture data in video animation.

Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights.

Analysis of cerebrospinal fluid (CSF) is essential for diagnostic workup of patients with neurological diseases and includes differential cell typing. The current gold standard is based on microscopic examination by specialised technicians and neuropathologists, which is time-consuming, labour-intensive and subjective. We, therefore, developed an image analysis approach based on expert annotations of 123,181 digitised CSF objects from 78 patients corresponding to 15 clinically relevant categories and trained a multiclass convolutional neural network (CNN). The CNN classified the 15 categories with high accuracy (mean AUC 97.3%). By using explainable artificial intelligence (XAI), we demonstrate that the CNN identified meaningful cellular substructures in CSF cells recapitulating human pattern recognition. Based on the evaluation of 511 cells selected from 12 different CSF samples, we validated the CNN by comparing it with seven board-certified neuropathologists blinded for clinical information. Inter-rater agreement between the CNN and the ground truth was non-inferior (Krippendorff's alpha 0.79) compared with the agreement of seven human raters and the ground truth (mean Krippendorff's alpha 0.72, range 0.56-0.81). The CNN assigned the correct diagnostic label (inflammatory, haemorrhagic or neoplastic) in 10 out of 11 clinical samples, compared with 7-11 out of 11 by human raters. Our approach provides the basis to overcome current limitations in automated cell classification for routine diagnostics and demonstrates how a visual explanation framework can connect machine decision-making with cell properties and thus provide a novel versatile and quantitative method for investigating CSF manifestations of various neurological diseases.

School Mode Choice Classification Model Exploitation Though Artificial Intelligence Classification Application

Human behavior pattern recognition exploited using Artificial Neural Networks (ANN) is at the core of Artificial Intelligence (AI) classification applications. School trip transport forecasting mode selection based upon ANN forecasting ability provided an easy-to-use scientific toolkit for sustainable urban policies designing and implementation. The paper capitalized acknowledged forecasting ability of ANN to recognize and classify behavior patterns leading to parental decisions of school mode choice. Main stages of this research included the conduction of an extended questionnaire survey in 512 parents of school students in Thessaloniki (northern Greece) and the investigation whether ANN forecasting model could classify school mode choice. Research provided promising results (forecasting ability ranging from 76% to 93%) on school mode parental selection forecasting models based on ANN classifier, providing a solid proof of concept for further investigation.

Identifying task-relevant features for human pattern recognition under blur: Insights from deep learning

Identifying task-relevant features for human pattern recognition under blur: Insights from deep learning

ImageSchemaNet: A framester graph for embodied commonsense knowledge

Commonsense knowledge is a broad and challenging area of research which investigates our understanding of the world as well as human assumptions about reality. Deriving directly from the subjective perception of the external world, it is intrinsically intertwined with embodied cognition. Commonsense reasoning is linked to human sense-making, pattern recognition and knowledge framing abilities. This work presents a new resource that formalizes the cognitive theory of image schemas. Image schemas are dynamic conceptual building blocks originating from our sensorimotor interactions with the physical world, and enable our sense-making cognitive activity to assign coherence and structure to entities, events and situations we experience everyday. ImageSchemaNet is an ontology that aligns pre-existing resources, such as FrameNet, VerbNet, WordNet and MetaNet from the Framester hub, to image schema theory. This article describes an empirical application of ImageSchemaNet, combined with semantic parsers, on the task of annotating natural language sentences with image schemas.

Innate Immunity Crosstalk with Helicobacter pylori: Pattern Recognition Receptors and Cellular Responses

Helicobacter pylori is one of the most successful gastric pathogens that has co-existed with human for centuries. H. pylori is recognized by the host immune system through human pattern recognition receptors (PRRs), such as toll-like receptors (TLRs), C-type lectin like receptors (CLRs), NOD-like receptors (NLRs), and RIG-I-like receptors (RLRs), which activate downstream signaling pathways. Following bacterial recognition, the first responders of the innate immune system, including neutrophils, macrophages, and dendritic cells, eradicate the bacteria through phagocytic and inflammatory reaction. This review provides current understanding of the interaction between the innate arm of host immunity and H. pylori, by summarizing H. pylori recognition by PRRs, and the subsequent signaling pathway activation in host innate immune cells.

Human (but not animal) motion can be recognized at first sight – After treatment for congenital blindness

The long-standing nativist vs. empiricist debate asks a foundational question in epistemology – does our knowledge arise through experience or is it available innately? Studies that probe the sensitivity of newborns and patients recovering from congenital blindness are central in informing this dialogue.One of the most robust sensitivities our visual system possesses is to ‘biological motion’ – the movement patterns of humans and other vertebrates. Various biological motion perception skills (such as distinguishing between movement of human and non-human animals, or between upright and inverted human movement) become evident within the first months of life. The mechanisms of acquiring these capabilities, and specifically the contribution of visual experience to their development, are still under debate.We had the opportunity to directly examine the role of visual experience in biological motion perception, by testing what level of sensitivity is present immediately upon onset of sight following years of congenital visual deprivation. Two congenitally blind patients who underwent sight-restorative cataract-removal surgery late in life (at the ages of 7 and 20 years) were tested before and after sight restoration. The patients were shown displays of walking humans, pigeons, and cats, and asked to describe what they saw.Visual recognition of movement patterns emerged immediately upon eye-opening following surgery, when the patients spontaneously began to identify human, but not animal, biological motion. This recognition ability was evident contemporaneously for upright and inverted human displays.These findings suggest that visual recognition of human motion patterns may not critically depend on visual experience, as it was evident upon first exposure to un-obstructed sight in patients with very limited prior visual exposure, and furthermore, was not limited to the typical (upright) orientation of humans in real-life settings.

Design of Intelligent Perception Module Based on Wireless Sensor Network and Basketball Sports Attitude

In recent years, along with microelectromechanical (MEMS) technique and wireless body area network (WBAN), wearable health monitoring systems have emerged. With sustainability posture detection technique, people pay more and more attention to mankind posture detection. Human posture detection technique has been widely used in medical, film and television, industry, sports, and other fields. Motion capture is a technique for measuring the motion of moving objects in three-dimensional space with great accuracy. It is the most efficient method for producing computer 3D animation and collecting human motion data. The use of motion capture systems in animation is becoming increasingly common. The problem of a single sensor having a large error in monitoring human motion and attitude is addressed. The use of multisensor data fusion technology is used to propose a human motion pattern recognition method based on data fusion of accelerometer and gyroscope. The system must effectively integrate the information of various sensors in order to achieve the goal of accuracy, timeliness, and reliability processing, and multisensor information fusion systems for various complex application objects are constantly emerging.

Recognition and inhibition of SARS-CoV-2 by humoral innate immunity pattern recognition molecules.

The humoral arm of innate immunity includes diverse molecules with antibody-like functions, some of which serve as disease severity biomarkers in coronavirus disease 2019 (COVID-19). The present study was designed to conduct a systematic investigation of the interaction of human humoral fluid-phase pattern recognition molecules (PRMs) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Of 12 PRMs tested, the long pentraxin 3 (PTX3) and mannose-binding lectin (MBL) bound the viral nucleocapsid and spike proteins, respectively. MBL bound trimeric spike protein, including that of variants of concern (VoC), in a glycan-dependent manner and inhibited SARS-CoV-2 in three in vitro models. Moreover, after binding to spike protein, MBL activated the lectin pathway of complement activation. Based on retention of glycosylation sites and modeling, MBL was predicted to recognize the Omicron VoC. Genetic polymorphisms at the MBL2 locus were associated with disease severity. These results suggest that selected humoral fluid-phase PRMs can play an important role in resistance to, and pathogenesis of, COVID-19, a finding with translational implications.

Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms.

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.

Non‐invasive AI model for human functional patterns recognition in IADLs

AbstractBackgroundAlzheimer's Disease (AD) diagnosis at early stages currently represents an important challenge for the scientific community, which is gradually accentuated due to the global perspective of population aging. Current clinical processes for the diagnosis of this disease are increasingly effective; these include invasive tests of nervous system biomarkers, which are complemented by non‐invasive tests of human cognitive and functional performance, such as the mini‐mental state examination and the analysis of Instrumental Activities of Daily Living (IADLs).MethodThe present work is centered around the development of technological tools for creating an automated model to support the diagnosis of early AD disease. We present a novel non‐invasive methodology for the development of an Artificial Intelligence‐based model, which analyzes human biomechanical markers of IADLs activities to recognize human functional patterns. For the development of this model, we have built a dataset of egocentric videos containing IADLs activities, organized in four classes, based on the prehensile patterns of the hands: strength and precision, and on the kinematics of the instruments: displacement and manipulation. We have characterized the dataset using mathematical methods to get information to directly emulate the relationship with Lawton and Brody's geriatric test, which is used in clinical protocols to estimate human functional capacity. This characterization relationship between biomechanical markers and human functional patterns represents a benefit for quantitative and objective assessment in support of geriatric evaluation and patient follow‐up.ResultOur proposed model results in an accuracy of 73.74% in the recognition of human functional patterns related to the kinematics of the instruments, 59.84% in the analysis of the prehensile pattern of the hands, and 48.5% when the classes were recognized independently.ConclusionThis allows us to establish in a quantifiable way performance region benchmarks of human functional capacity for IADLs activities, by obtaining a support model in the diagnostic evaluation of AD disease at early stages. Our proposed model allows us to establish the guideline to improve the automatic recognition of human functional patterns, of which we obtained an acceptable percentage testing instruments kinematics', followed by the hands' prehensile patterns.

Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

With the development of science and technology, the introduction of virtual reality technology has pushed the development of human-computer interaction technology to a new height. The combination of virtual reality and human-computer interaction technology has been applied more and more in military simulation, medical rehabilitation, game creation, and other fields. Action is the basis of human behavior. Among them, human behavior and action analysis is an important research direction. In human behavior and action, recognition research based on behavior and action has the characteristics of convenience, intuition, strong interaction, rich expression information, and so on. It has become the first choice of many researchers for human behavior analysis. However, human motion and motion pictures are complex objects with many ambiguous factors, which are difficult to express and process. Traditional motion recognition is usually based on two-dimensional color images, while two-dimensional RGB images are vulnerable to background disturbance, light, environment, and other factors that interfere with human target detection. In recent years, more and more researchers have begun to use fuzzy mathematics theory to identify human behaviors. The plantar pressure data under different motion modes were collected through experiments, and the current gait information was analyzed. The key gait events including toe-off and heel touch were identified by dynamic baseline monitoring. For the error monitoring of key gait events, the screen window is used to filter the repeated recognition events in a certain period of time, which greatly improves the recognition accuracy and provides important gait information for motion pattern recognition. The similarity matching is performed on each template, the correct rate of motion feature extraction is 90.2%, and the correct rate of motion pattern recognition is 96.3%, which verifies the feasibility and effectiveness of human motion recognition based on fuzzy theory. It is hoped to provide processing techniques and application examples for artificial intelligence recognition applications.

Human motion pattern recognition method and experiment based on dynamic time warping

In order to make the exoskeleton with better wearing effect, it is necessary to improve the exoskeleton motion intention recognition. In this paper, thin-film pressure sensors and inertial sensors are used to acquire human motion information on a wire-driven exoskeleton robot. The lower limb exoskeleton uses DTW (Dynamic Time Wraping) algorithm to recognize four kinds of human motion patterns by comparing the collected data with database features. The result shows that DTW algorithm can effectively recognize human motion patterns.

Personal‐specific gait recognition based on latent orthogonal feature space

Exoskeleton has been applied in the field of medical rehabilitation and assistance. However, there are still some problems in the interaction between human and exoskeleton, such as time delay, the existence of certain constraints on the human body, and the movement in time is hard to follow. A human motion pattern recognition model based on the long short-term memory (LSTM) is proposed, which can recognise the state of the human body. Meanwhile, the orthogonalisation method is integrated to make personal-specific disentangling, and it can effectively improve the generalisation ability of different groups of people, so as to improve the effective follower ability of the exoskeleton. Compared with some other traditional methods, this model has better performance and stronger generalisation ability, which has certain significance in the field of exoskeleton algorithm.

An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system’s flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.

Method of Analyzing and Managing Volleyball Action by Using Action Sensor of Mobile Device

With the development of electronic technology and sensor technology, more and more intelligent electronic devices integrate micro inertial sensors, which makes the research of human action recognition based on action sensing data have great application value. Data‐based action recognition is a new research direction in the field of pattern recognition, which is essentially a process of action data acquisition, feature extraction, feature extraction, and recognition, the process of classification and recognition. Inertial motion information includes acceleration and angular velocity information, which is ubiquitous in daily life. Compared with motion recognition based on visual information, it can more directly reflect the meaning of action. This study mainly discusses the method of analyzing and managing volleyball action by using the action sensor of mobile device. Based on the motion recognition algorithm of support vector machine, the motion recognition process of support vector machine is constructed. When the data terminal and gateway of volleyball players are not in the same LAN, the classification algorithm classifies the samples to be tested through the characteristic data, which directly affects the recognition results. In this paper, the support vector machine algorithm is selected as the data classification algorithm, and the calculation of the classification process is reduced by designing an appropriate kernel function. For multiclass problems, the hierarchical structure of directed acyclic graph is optimized to improve the recognition rate. We need to bind motion sensors to human joints. In order to realize real‐time recognition of human motion, mobile devices need to add windows to the motion capture data, that is, divide the data into a small sequence of specified length, and provide more application scenarios for the device. This method of embedding motion sensors into devices to read motion information is widely used, which provides a convenient data acquisition method for human motion pattern recognition based on motion information. The multiclassification support vector machine algorithm is used to train the classification algorithm model with action data. When the signal strength of the sensor is 90 t and the speed is 2.0 m/s and 0.5 m/s, the detection accuracy of the adaptive threshold is 93% and 95%, respectively. The results show that the SVM method based on hybrid kernel function can greatly improve the recognition accuracy of volleyball stroke, and the recognition time is short.

Research on Hand Action Pattern Recognition of Bionic Limb Based on Surface Electromyography

Hands are important parts of a human body. It is not only the main tool for people to engage in productive labor, but also an important communication tool. When the hand moves, the human body produces a kind of signal named surface electromyography (sEMG), which is a kind of electrophysiological signal that accompanies muscle activity. It contains a lot of information about human movement consciousness. The bionic limb is driven by multi-degree-freedom control, which is got by converting the recognition result and this can meet the urgent need of people with disabilities for autonomous operation. A profound study of hand action pattern technology based on sEMG signals can achieve the ability of the bionic limb to distinguish the hand action fast and accurately. From the perspective of the pattern recognition of the bionic limb, this paper discussed the human hand action pattern recognition technology of sEMG. By analyzing and summarizing the current development of human hand movement recognition, the author proposed a bionic limb schema based on artificial neural network and the improved DT-SVM hand action recognition system. According to the research results, it is necessary to expand the type and total amount of hand movements and gesture recognition, in order to adapt to the objective requirements of the diversity of hand action patterns in the application of the bionic limb.

Monoclonal Antibody to CD14, TLR4, or CD11b: Impact of Epitope and Isotype Specificity on ROS Generation by Human Granulocytes and Monocytes.

Lipopolysaccharides (LPSs or endotoxins) from Gram-negative bacteria represent pathogen-associated molecular patterns (PAMPs) that are recognized by CD14 and Toll-like receptor 4 (TLR4). Lipopolysaccharides prime polymorphonuclear leukocytes (PMNs) for substantial production of reactive oxygen species (ROS) during its response to secondary stimuli such as chemoattractants or pathogens. The excessive ROS production can damage surrounding host tissues, thereby amplifying the inflammatory reaction caused by pathogens. Today, specific antibodies against CD14, TLR4, and CD11b are being used as the essential tools to elucidate the role of these receptors in acute inflammation and some of these antibodies have advised as therapeutic agents for clinical use. Because each antibody has two antigen-binding arms [F(ab′)2] and one Fc arm, its effect on cellular response is much more complicated rather than simple blockage of target receptor. In fact, IgG antibody, once bound to target receptor, engages Fc receptors γ (FcγRs) and thereby is able to activate the adaptive immune system. The consequences of antibody-dependent binary heterotypic association of CD14, TLR4, or CD11b with FcγRs as well as homotypic one on ROS production are not well elucidated. Moreover, the consequences of antigenic recognition of CD14, TLR4, or CD11b by specific F(ab′)2 fragments are not always investigated. In this review, we will discuss known mechanisms underlying the therapeutic efficiency of CD14, TLR4, and CD11b/CD18 antibodies with a focus on LPS-dependent ROS or cytokine production by PMNs or monocytes. The impacts of F(ab′)2 as well as antibody IgG subclasses (isotypes) in therapeutic efficiency or agonistic potency of known antibodies against abovementioned receptors are presented. We also pay attention to how the efficiency of different IgG antibody subclasses is modulated during LPS-induced inflammation and by production of priming agents such as interferon γ (IFN-γ). Our review reinforces the molecular targets and therapeutic approaches to amelioration of harmful consequences of excessive activation of human pattern recognition receptors.

Finite Class Bayesian Inference System for Circle and Linear Walking Gait Event Recognition Using Inertial Measurement Units.

Accurate and fast human motion pattern recognition is the key to ensuring lower limb assistive devices' appropriate assistance. The research on human motion pattern recognition of lower limb assistive devices mainly focuses on sagittal gait. The motion pattern such as circular walking (CW) is asymmetric about the sagittal plane of the body. CW is common in daily living. However, the recognition algorithm of CW is rarely reported. Since lower limb assistive devices interact with humans, lacking the capability of recognizing CW is dangerous. Thus, to realize the accurate and fast recognition of CW, this article proposed a finite class Bayesian interference system (FC-BesIS). FC-BesIS is designed to recognize walking activities (linear walking and CW) and gait events (heel contact, load response, mid stance, terminal stance, pre-swing, initial swing, mid swing, and terminal swing). A finite class method which reduces the number of potential classes according to elimination rules before decision-making is introduced. Elimination rules are designed based on likelihood estimation and sensor information. The experiments show that walking activities and gait events can be accurately and fastly recognized by FC-BesIS. The experiments also show that the performance of FC-BesIS in mean recognition accuracy (MRA) and mean decision time (MDT) is improved compared with BesIS. The MRA of walking activities and gait events are 100% and 97.38%, respectively. The MDT of walking activities and gait events are 28.19 ms and 33.94 ms, respectively. Overall, FC-BesIS has been proved to be an accurate and fast recognition algorithm for human motion patterns using wearable sensors.