IMU Sensing–Based Hopfield Neuromorphic Computing for Human Activity Recognition

Aiming at the self-association feature of the Hopfield neural network, we can reduce the need for extensive sensor training samples during human behavior recognition. For a training algorithm to obtain a general activity feature template with only one time data preprocessing, this work proposes a data preprocessing framework that is suitable for neuromorphic computing. Based on the preprocessing method of the construction matrix and feature extraction, we achieved simplification and improvement in the classification of output of the Hopfield neuromorphic algorithm. We assigned different samples to neurons by constructing a feature matrix, which changed the weights of different categories to classify sensor data. Meanwhile, the preprocessing realizes the sensor data fusion process, which helps improve the classification accuracy and avoids falling into the local optimal value caused by single sensor data. Experimental results show that the framework has high classification accuracy with necessary robustness. Using the proposed method, the classification and recognition accuracy of the Hopfield neuromorphic algorithm on the three classes of human activities is 96.3%. Compared with traditional machine learning algorithms, the proposed framework only requires learning samples once to get the feature matrix for human activities, complementing the limited sample databases while improving the classification accuracy.

Multi-modal Data Fusion Method for Human Behavior Recognition Based on Two IA-Net and CHMM

The multi-modal data fusion method based on IA-net and CHMM technical proposed is designed to solve the problem that the incompleteness of target behavior information in complex family environment leads to the low accuracy of human behavior recognition.The two improved neural networks(STA-ResNet50、STA-GoogleNet)are combined with LSTM to form two IA-Nets respectively to extract RGB and skeleton modal behavior features in video. The two modal feature sequences are input CHMM to construct the probability fusion model of multi-modal behavior recognition.The experimental results show that the human behavior recognition model proposed in this paper has higher accuracy than the previous fusion methods on HMDB51 and UCF101 datasets. New contributions: attention mechanism is introduced to improve the efficiency of video target feature extraction and utilization. A skeleton based feature extraction framework is proposed, which can be used for human behavior recognition in complex environment. In the field of human behavior recognition, probability theory and neural network are cleverly combined and applied, which provides a new method for multi-modal information fusion.

Research on Face Recognition Sports Intelligence Training Platform Based on Artificial Intelligence

With the application of artificial intelligence in many social fields, the research of human behavior recognition and non-contact detection of human physiological parameters based on face recognition and other technologies has developed rapidly, and the application of artificial intelligence in culture, sports and entertainment has also begun to rise. How to apply the existing mature technology to the sports intelligence training system taking table tennis as an example is a hot issue worthy of study. In this paper, a comprehensive intelligent table tennis training system and platform based on Convolutional Neural Network face recognition and face heart rate detection is designed, which is mainly used to solve the philosophical training problem in table tennis. In the system place, an identification cameras is set at the entrance of table tennis training places, which is used for table tennis players’ sign-in and training table number allocation, and an intelligent analysis cameras is set above each intelligent training table, which is used for detecting the face and heart rate of table tennis players. Each intelligent training platform consists of intelligent voice control unit, server, camera, industrial control computer, monitor and other terminal modules. The member data center constitutes the platform of intelligent table tennis training system.

Mobile Information System for English Teaching Based on Edge Computing and VR

There are many difficulties in using cloud computing technology to solve this problem, and edge computing technology just provides an effective way to solve this problem. Changing from two-dimensional to three-dimensional will make education more realistic. This article uses the VR technology algorithm to design and analyze the English teaching system, combined with the current environment and requirements, in the construction of teaching resources, in order to clarify the direction in practical application. Secondly, according to the scene of human behavior recognition, a human behavior data collection, processing and analysis system based on edge computing is designed and implemented. And on the basis of detailed analysis of VR technology, the theoretical basis of VR technology applied to experiment teaching is discussed, and the development of virtual experiment is analyzed concretely using systematic teaching design ideas. VR technology greatly stimulates students’ learning motivation but also significantly improves their interest in learning. In addition, the application of virtual technology has greatly improved students’ learning enthusiasm. According to the findings of this article, based on the application of virtual reality technology, students’ English comprehension skills have increased by 19%, and the degree of resource sharing has increased by 11%.

Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants’ body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.

Human Behavior Recognition Method based on Two-layer LSTM Network with Attention Mechanism

Aiming at the problem that the exiting human skeleton-based action recognition methods cannot fully extract the relevant information before and after the action, resulting in low utilization efficiency of skeleton points, we propose a two-layer LSTM (long short term memory) network with attention mechanism. The network has two layers, the first LSTM network is used for skeleton coding and initialization of system storage units and the second LSTM network integrates attention mechanism to further process the data of the first layer network. An algorithm is designed to assign different weights to skeleton points according to the importance of human body, which greatly increases the recognition accuracy. Action classification is accomplished by multiple support vector machines. Through training and testing, the average recognition rate of 98.5% is achieved on KTH dataset. The experimental result shows that the proposed method is effective in human behavior recognition.