Human Motion Pattern Recognition Research Articles

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

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

Human Motion Pattern Recognition Based on Nano-sensor and Deep Learning

A human motion pattern recognition algorithm based on Nano-sensor and deep learning is studied to recognize human motion patterns in real time and with high accuracy. First, human motion data are collected by micro electro mechanical system, and the noise in such data is filtered by smoothing filtering method to obtain high-quality motion data. Second, key time-domain features are extracted from high-quality motion data. Finally, after fusing and processing the key time-domain features, it is input into the deep long and short-term memory (LSTM) neural network to build a deep LSTM human motion pattern recognition model and complete human motion pattern recognition. The results show that the proposed algorithm can realize the recognition of various motion patterns with high accuracy of data acquisition, the average recognition accuracy is 94.8%, the average recall reaches 89.7%, and the F1 score of the algorithm are high, and the recognition time consuming is short, which can realize accurate and efficient human motion pattern recognition and provide guarantee for effective monitoring of the target human motion health.

Human motion pattern recognition based on the fused random forest algorithm

In this paper, a fused random forest algorithm named (PSO-RF)-(KNN-HC) is proposed for the recognition of seven human motion patterns, including flat walking, sitting, standing, going up the stairs, going down the stairs, going up the slope and going down the slope. A particle swarm optimization (PSO) method is used to find the optimal parameters of the random forest model and build the optimal classification model. In the decision process of the random forest, the algorithm of k-nearest neighbors-hierarchical clustering (KNN-HC) is applied to select the decision trees for new recognition samples and calculate the voting weights of each tree, which improves the classification accuracy of the random forest model for multi-classification problems. In the data processing stage, the motion data are analyzed from view of the frequency domain using the fast Fourier transform (FFT) to divide the data segments in cycles and perform feature extraction. Finally, the proposed algorithm is validated against other machine learning algorithms based on a self-constructed human motion dataset through a real motion data acquisition platform, and the effectiveness of the proposed method is also validated on an open source dataset.

Dual-Layer All-Textile Flexible Pressure Sensor Coupled by Silver Nanowires with Ti3C2-Mxene for Monitoring Athletic Motion during Sports and Transmitting Information.

At present, wearable flexible pressure sensors have broad application prospects in fields such as motion monitoring and information transmission. However, it is still a challenge to design flexible pressure sensors with high sensitivity over a large sensing range and simple fabrication. Here, we use a simple "dipping-drying" method to fabricate a fabric-based flexible pressure sensor by coupling silver nanowires (AgNWs) with Ti3C2-MXene. The interaction between MXene and AgNWs helps realize a dual-layer sensing network, achieving good synergistic effects between pressure sensitivity and sensing range. The effects of the material combination and dip-coating sequence on the sensor's performance are systematically studied. The results show that the sensor was impregnated sequentially with AgNWs solution, and the MXene solution has the highest sensitivity (0.168 kPa-1) over a wide range (190 kPa). Meanwhile, it has the advantages of low response hysteresis and detection limit, as well as good linearity and durability. We further demonstrate the application of this sensor in human physiological signal monitoring and motion pattern recognition. It can also encrypt and transmit information according to different pressing states. In addition, the proposed pressure sensor array exhibits spatial resolution detection capabilities, laying the foundation for applications in the fields of motion monitoring and human-computer interaction.

Self-Repairing and Energy-Harvesting Triboelectric Sensor for Tracking Limb Motion and Identifying Breathing Patterns.

The increasing prevalence of health problems stemming from sedentary lifestyles and evolving workplace cultures has placed a substantial burden on healthcare systems. Consequently, remote health wearable monitoring systems have emerged as essential tools to track individuals' health and well-being. Self-powered triboelectric nanogenerators (TENGs) have exhibited significant potential for use as emerging detection devices capable of recognizing body movements and monitoring breathing patterns. However, several challenges remain to be addressed in order to fulfill the requirements for self-healing ability, air permeability, energy harvesting, and suitable sensing materials. These materials must possess high flexibility, be lightweight, and have excellent triboelectric charging effects in both electropositive and electronegative layers. In this work, we investigated self-healable electrospun polybutadiene-based urethane (PBU) as a positive triboelectric layer and titanium carbide (Ti3C2Tx) MXene as a negative triboelectric layer for the fabrication of an energy-harvesting TENG device. PBU consists of maleimide and furfuryl components as well as hydrogen bonds that trigger the Diels-Alder reaction, contributing to its self-healing properties. Moreover, this urethane incorporates a multitude of carbonyl and amine groups, which create dipole moments in both the stiff and the flexible segments of the polymer. This characteristic positively influences the triboelectric qualities of PBU by facilitating electron transfer between contacting materials, ultimately resulting in high output performance. We employed this device for sensing applications to monitor human motion and breathing pattern recognition. The soft and fibrous-structured TENG generates a high and stable open-circuit voltage of up to 30 V and a short-circuit current of 4 μA at an operation frequency of 4.0 Hz, demonstrating remarkable cyclic stability. A significant feature of our TENG is its self-healing ability, which allows for the restoration of its functionality and performance after sustaining damage. This characteristic has been achieved through the utilization of the self-healable PBU fibers, which can be repaired via a simple vapor solvent method. This innovative approach enables the TENG device to maintain optimal performance and continue functioning effectively even after multiple uses. After integration with a rectifier, the TENG can charge various capacitors and power 120 LEDs. Moreover, we employed the TENG as a self-powered active motion sensor, attaching it to the human body to monitor various body movements for energy-harvesting and sensing purposes. Additionally, the device demonstrates the capability to recognize breathing patterns in real time, offering valuable insights into an individual's respiratory health.

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.

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.

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.

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.

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.

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.

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.

Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks

In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.

Fingerprint-inspired graphene pressure sensor with wrinkled structure

Nowadays, flexible pressure sensors have broad applications in healthcare monitoring, wearable electronics and so on. However, conventional fabrications are mostly time-consuming, complicated and even use toxic chemical reagents. It is in urgent need to fabricate pressure sensors with high sensitivity in a facile way. In nature, the fingertip skin of human has many epidermal ridges, which can sense small external stimuli. Here, inspired from the fingerprint, we propose a new type of pressure sensor made with reduced graphene oxide (rGO). The rGO film is designed to form a wrinkled structure by a facile stretching-releasing process, which mimics the morphology of fingertip skin. Owing to the fingertip-skin-like wrinkled structure, the pressure sensor exhibits high sensitivity of 5.77 kPa−1 (0 - 490 Pa). It also has a lot of attractive properties, such as fast response (< 100 ms), small detecting pressure (3 Pa) and good repeatability. We show the ability of the pressure sensor in small pressure detecting, human motion monitoring and motion pattern recognition. Furthermore, a translation system and a multi-touch device with spatially resolved detection based on the pressure sensor are developed. This pressure sensor is hopeful to have potential applications in wearable electronics and human–machine interaction systems.

Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion.

Aiming at the requirement of rapid recognition of the wearer’s gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.

RECOGNITION OF SEMG HAND ACTIONS BASED ON CLOUD ADAPTIVE QUANTUM CHAOS IONS MOTION ALGORITHM OPTIMIZED SVM

In this study, in order to improve the accuracy of human hand motion pattern recognition, a novel pattern recognition method for optimizing the support vector machine (SVM) by using a cloud adaptive quantum chaos ions motion optimization (AQCIMO-SVM) algorithm is proposed. The maximum values of wavelet coefficients were extracted as feature samples from the de-noised surface electromyography (sEMG) signals, which were collected from the forearm muscles of several subjects, and then the extracted feature was inputted into an SVM to classify action recognition. In addition, the AQCIMO algorithm was applied to optimize the penalty parameters and the kernel parameters of the SVM, which are used to avoid the uncertainty and complexity of parameter selection and improve the recognition precision of the model, thus improving the model recognition accuracy. The simulation results demonstrated that the two types of movement, which included basic gestures (rest, hand grasp, hand extension, wrist down, and wrist up) and object grabbing gestures (pre-grab, grab, transport and place, release hand, and return to the original position) were successfully identified by the SVM method combined with the AQCIMO algorithm. Compared to mainstream and classic classifiers, namely, GA-SVM, PSO-SVM, and AFSA-SVM, the accuracy of the proposed method was higher by 4.2% to 8.2% than that of the aforementioned classifiers. Therefore, the AQCIMO-SVM algorithm can efficiently solve the problem of the classification of the action pattern of the sEMG signals, which has a very important practical value.

Human Action Pattern Recognition and Semantic Research Based on Embodied Cognition Theory

Human body motion pattern recognition in video images is an important research direction in the field of pattern recognition. It has a very broad application prospect in many fields such as intelligent video surveillance, human-computer interaction, motion analysis, video retrieval, etc. Research has also received extensive attention from scholars at home and abroad. Pattern recognition is essentially a branch of artificial intelligence. It has its unique role in the field of artificial intelligence. Accurate recognition of human body motion patterns in video images is of great help in image classification, retrieval, human tracking and video surveillance. Based on the human visual perception mechanism, this paper proposes a human behavior recognition algorithm based on semantic saliency map. Through the combination of sliding window and similarity measure, the behavioral region that best exhibits the semantic features of the image is found, which is the semantically significant region. The semantic significant region and the original image are used as the dual input source to study the human behavior recognition, and the image is enhanced. The utilization of significant regional information better reveals the identifiable area of the image and contributes to the recognition of human behavior.

Design of human activity recognition algorithms based on a single wearable IMU sensor

In recent years, with the rapid development of inertial measurement unit (IMU) technology, wireless body area network and pattern recognition theory, human motion recognition based on wearable technology has gradually gained the attention of researchers. In this paper, the human activity recognition method based on wearable sensor motion information fusion is studied. On the existing wearable system platform, the time domain analysis and frequency domain analysis of human motion information are used to distinguish the daily activity of the human body, and based on the human motion data acquisition experiment, time domain features, frequency domain features and attitude angles of the human motion data are used as identification features. On that basis multi-classification activity recognition algorithm based on support vector machine is proposed and human motion pattern recognition is carried out. The experimental results show that the system can accurately identify the daily activity of the human body.

Design of human activity recognition algorithms based on a single wearable IMU sensor

In recent years, with the rapid development of inertial measurement unit (IMU) technology, wireless body area network and pattern recognition theory, human motion recognition based on wearable technology has gradually gained the attention of researchers. In this paper, the human activity recognition method based on wearable sensor motion information fusion is studied. On the existing wearable system platform, the time domain analysis and frequency domain analysis of human motion information are used to distinguish the daily activity of the human body, and based on the human motion data acquisition experiment, time domain features, frequency domain features and attitude angles of the human motion data are used as identification features. On that basis multi-classification activity recognition algorithm based on support vector machine is proposed and human motion pattern recognition is carried out. The experimental results show that the system can accurately identify the daily activity of the human body.