Posture Recognition Algorithm Research Articles

A dual fusion recognition model for sleep posture based on air mattress pressure detection

In order to solve the difficult portability problem of traditional non-invasive sleeping posture recognition algorithms arising from the production cost and computational cost, this paper proposes a sleeping posture recognition model focusing on human body structural feature extraction and integration of feature space and algorithms based on a specific air-spring mattress structure, called SPR-DE (SPR-DE is the Sleep Posture Recognition-Data Ensemble acronym form). The model combines SMR (SMR stands for Principle of Spearman Maximal Relevance) with horizontal and vertical division based on the barometric pressure signals in the human body’s backbone region to reconstruct the raw pressure data into strongly correlated non-image features of the sleep postures in different parts and directions and construct the feature set. Finally, the recognit-ion of the two sleep postures is accomplished using the AdaBoost-SVM integrated classifier. SPR-DE is compared with the base and integrated classifiers to verify its performance. The experimental results show that the amount of significant features helps the algorithm to classify different sleeping patterns more accurately, and the f1 score of the SPR-DE model determined by the comparison experiments is 0.998, and the accuracy can reach 99.9%. Compared with other models, the accuracy is improved by 2.9% ~ 7.7%, and the f1-score is improved by 0.029 ~ 0.076. Therefore, it is concluded that the SMR feature extraction strategy in the SPR-DE model and the AdaBoost-SVM can achieve high accuracy and strong robustness in the task of sleep posture recognition in a small area, low-density air-pressure mattress, taking into account the comfort of the mattress structural design and the sleep posture recognition, integrated with the mattress adaptive adjustment system.

Visual Perception and Multimodal Control: A Novel Approach to Designing an Intelligent Badminton Serving Device

Addressing the current issue of limited control methods for badminton serving devices, this paper proposes a vision-based multimodal control system and method for badminton serving. The system integrates computer vision recognition technology with traditional control methods for badminton serving devices. By installing vision capture devices on the serving device, the system identifies various human body postures. Based on the content of posture information, corresponding control signals are sent to adjust parameters such as launch angle and speed, enabling multiple modes of serving. Firstly, the hardware design for the badminton serving device is presented, including the design of the actuator module through 3D modeling. Simultaneously, an embedded development board circuit is designed to meet the requirements of multimodal control. Secondly, in the aspect of visual perception for human body recognition, an improved BlazePose candidate region posture recognition algorithm is proposed based on existing posture recognition algorithms. Furthermore, mappings between posture information and hand information are established to facilitate parameter conversion for the serving device under different postures. Finally, extensive experiments validate the feasibility and stability of the developed system and method.

Application of Posture Recognition Model Based on Improved CORDIC Algorithm in Spatial Health Monitoring of the Elderly Living Alone

In an aging society, accidental falls are highly likely to occur due to external factors or the incidence of diseases in the elderly. Serious consequences can arise when an elderly person living alone falls and is unable to get up to call for help. To address the issue of automatic assistance for elderly individuals living alone when they fall by accident, and the problem of behavioral analysis in daily life, this paper designs and develops an elderly monitoring system through the study of feature extraction algorithms and posture recognition algorithms. Furthermore, this paper has also developed a set of three-dimensional spatial angle calculation methods suitable for implementation on a microcontroller. The CORDIC algorithm can compute two-dimensional spatial angles through simple addition, subtraction, and bit shifting operations. Through careful study of the CORDIC algorithm, it was found that applying the CORDIC algorithm twice can achieve the calculation of three-dimensional spatial angles. Through experimental testing, we can see that the algorithms proposed in this paper have achieved good experimental results in terms of computational accuracy and operation time, reaching a relatively advanced level.

Efficient Underground Tunnel Place Recognition Algorithm Based on Farthest Point Subsampling and Dual-Attention Transformer.

An autonomous place recognition system is essential for scenarios where GPS is useless, such as underground tunnels. However, it is difficult to use existing algorithms to fully utilize the small number of effective features in underground tunnel data, and recognition accuracy is difficult to guarantee. In order to solve this challenge, an efficient point cloud position recognition algorithm, named Dual-Attention Transformer Network (DAT-Net), is proposed in this paper. The algorithm firstly adopts the farthest point downsampling module to eliminate the invalid redundant points in the point cloud data and retain the basic shape of the point cloud, which reduces the size of the point cloud and, at the same time, reduces the influence of the invalid point cloud on the data analysis. After that, this paper proposes the dual-attention Transformer module to facilitate local information exchange by utilizing the multi-head self-attention mechanism. It extracts local descriptors and integrates highly discriminative global descriptors based on global context with the help of a feature fusion layer to obtain a more accurate and robust global feature representation. Experimental results show that the method proposed in this paper achieves an average F1 score of 0.841 on the SubT-Tunnel dataset and outperforms many existing state-of-the-art algorithms in recognition accuracy and robustness tests.

Human Sleeping Posture Recognition Based on Sleeping Pressure Image

Pressure ulcer prevention is very necessary for patients with limited movement. Human sleeping posture recognition algorithm is the key technology of intelligent pressure ulcer prevention equipment. In this article, a sleeping pressure image acquisition system is designed. Based on the data collected by the system, we innovatively propose a trunk centerline prediction algorithm based on 1-D Gaussian template. The average prediction deviation of the algorithm is 1.10 pixels (the actual equivalent distance is 1.27 cm). Statistics show that the algorithm has high accuracy for volunteers with different shapes. In addition, we propose a new correction algorithm for sleeping pressure image, which can effectively improve the accuracy of the subsequent sleeping posture recognition algorithm. Finally, the cumulative pressure value of the sleeping pressure images in the vertical direction is extracted as the feature, and a support vector machine (SVM) with sigmoid kernel is used to classify the three basic sleeping postures. Its classification accuracy is up to 97.2%.

SEMG-Based Hand Posture Recognition and Visual Feedback Training for the Forearm Amputee.

sEMG-based gesture recognition is useful for human–computer interactions, especially for technology supporting rehabilitation training and the control of electric prostheses. However, high variability in the sEMG signals of untrained users degrades the performance of gesture recognition algorithms. In this study, the hand posture recognition algorithm and radar plot-based visual feedback training were developed using multichannel sEMG sensors. Ten healthy adults and one bilateral forearm amputee participated by repeating twelve hand postures ten times. The visual feedback training was performed for two days and five days in healthy adults and a forearm amputee, respectively. Artificial neural network classifiers were trained with two types of feature vectors: a single feature vector and a combination of feature vectors. The classification accuracy of the forearm amputee increased significantly after three days of hand posture training. These results indicate that the visual feedback training efficiently improved the performance of sEMG-based hand posture recognition by reducing variability in the sEMG signal. Furthermore, a bilateral forearm amputee was able to participate in the rehabilitation training by using a radar plot, and the radar plot-based visual feedback training would help the amputees to control various electric prostheses.

Computational Technologies in Internet of Things and Big Data Technology for Physical Exercise Rehabilitation System

Traditional exercise rehabilitation mode information is closed, it is difficult for users to obtain exercise rehabilitation information, and the exercise rehabilitation data of users are not fully utilized. With the combination of big data technology and the Internet of Things and exercise rehabilitation, we also face the problem of processing massive amounts of data. Whether these data can be used well has become the key to obtaining good business benefits and improving user experience. This paper applies the Internet of Things and big data technology to the physical exercise rehabilitation system to improve sports data processing and combine with the big data processing technology to explore the factors affecting exercise rehabilitation to improve the effectiveness of physical exercise rehabilitation. Moreover, this paper proposes a posture recognition algorithm based on human vision and defines the human posture described by the feature, which improves the classifier’s ability to classify different posture data. Finally, this paper carries out experimental analysis to verify the effect of the method proposed. The research results show that the method constructed in this paper has a good physical exercise rehabilitation effect.

Information Collection, Analysis, and Monitoring System of Children's Physical Training Based on Multisensor.

In order to obtain more children's physical training information and improve the accuracy of children's physical training monitoring, a multisensor-based children's physical training information collection, analysis, and monitoring system is proposed. In the process of physical training and sports training, people's physical training information collection is directly related to the level and effectiveness of physical training. With the combination of multisensor concept and sports training information collection, it can collect the key index data of sports mobilization in real time with the help of multiple sensors and information technology. Taking children's physical training as the object, this paper designs a multisensor physical training data information acquisition terminal, collects different training characteristic data with the help of multisensor equipment, and then comprehensively analyzes and monitors the physical information with the help of certain fusion technology, so as to construct a human posture recognition algorithm based on children's physical training information acquisition. Support vector machine and decision tree are used to classify children's different physical exercise states, and a relatively perfect algorithm architecture of human posture recognition is constructed. The results show that for two decision trees, each decision tree is trained with a total of 675 groups of data, and a total of 342 groups of data are verified and pruned. The two decision trees take 7.17 s and 7.32 s to complete the training process, respectively. It can be seen that when the number of training groups is equal, the training time of the two placement methods is close, so it can be considered that the two placement methods have little effect on the training speed of decision tree. The experimental data show that the design of children's physical training monitoring system in this paper has a certain market value.

Research on Aided Judgment of Rural Sports Posture Based on Deep Learning

With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports auxiliary training, to get rid of the state of traditional sports training purely relying on experience. The article proposes a deep learning BP neural network human body posture recognition algorithm and briefly introduces the human body motion posture. The purpose of this research was to use the powerful data processing, mining, and analysis functions of deep learning to train the massive data generated in competitive sports training and apply it to competitive sports training. It is committed to promoting the accuracy and analysis of competitive sports training. Refinement provides technical guidance for athletes’ training, promotes the scientific and informatized development of competitive sports training in China, and provides some reference methods for the research and application of deep learning in competitive sports training. The article’s research results show that (1) taking a rural area as an example, we recorded the exercise postures of rural athletes in five different states: static, upstairs, downstairs, walking, and running. Comparing the recognition rate and training time of The BP neural network algorithm, ABC-BP algorithm, AFS-BP algorithm, and ABC-AFS-BP algorithm, it can be found that in terms of recognition rate, ABC-AFS-BP algorithm, AFS-BP algorithm, and ABC-BP algorithm are better than traditional BP algorithm. Among them, the recognition rate of the ABC-AFS-BP algorithm is higher than that of the ABC-BP algorithm, but it takes slightly more time than the ABC-BP algorithm. In terms of training time, the ABC-BP algorithm takes less time, but the accuracy is lower than the ABC-AFS-BP algorithm; the ABC-AFS-BP algorithm has a greater improvement in time consumption than the AFS-BP model and can guarantee the recognition rate and accuracy, and the error rate curves of the four algorithms show that after 500 iterations of the training part, the iteration error value of the ABC-AFS-BP algorithm is the smallest. (2) We evaluated sports postures of athletes from a certain rural team and concluded that bad postures will have a certain impact on the body. Among them, more than 85% of athletes in football and basketball have pelvic rotation. The problem is that football players have reached 90% of the test sample. 60% of football players and basketball players have the problem of collapsed foot. The main problem of aerobic athletes is flat back and collapsed foot. More than 90% of badminton players have high and low shoulder problems, and more than 80% of them have neck problems, which is a very serious body posture problem. (3) Detecting the flexibility experiment of the BP posture detection algorithm, compared with the traditional motion posture recording method, we tested from the three aspects of recording motion accuracy, missed detection rate, and recording time. The result shows BP posture detection. The missing detection rate of the algorithm is low, basically maintained at about 2.0, the accuracy of recording actions is relatively high, generally maintained above 98%, and the highest is 99.15%, and the recording time is short, maintained at 3–4 minutes; comparing traditional posture detection with the BP attitude detection algorithm, the missed detection rate of the algorithm is relatively high, kept at 4–6, the action accuracy is lower than that of the BP attitude detection algorithm, generally kept at about 95%, and the recording time is kept at 5–6 minutes. The posture detection algorithm is more efficient.

The Effects of Yoga Exercise on Pelvic Floor Rehabilitation of Postpartum Women.

Rehabilitation of the pelvic floor after delivery is very important for women. Pelvic floor rehabilitation can speed up the recovery of the postpartum vagina and pelvic floor muscle tension and elasticity and have a good effect on the prevention and treatment of postpartum vaginal prolapse and relaxation, urinary incontinence and other pelvic floor disorders. Thus, this article focuses on yoga exercise to explore its impact on postpartum pelvic floor rehabilitation. This article uses electrical stimulation and the treatment of pelvic floor muscles combined with the posture recognition algorithm, the yoga rehabilitation training program that has the best effect on the parturient is obtained, and the yoga myoelectric stimulation combined method and the traditional myoelectric stimulation method are designed for comparison experiments. The experimental results show that the parturients who have undergone the combined method of yoga myoelectric stimulation, in the resting state, contraction state, and Valsalva state, the position of the bladder meridian, the position of the uterus, and the position of the rectal ampulla of the parturient have a significant recovery compared those who have undergone the traditional electromyography treatment. In addition, the average area of hiatus in the pelvic floor ultrasound examination in the control group 42 days postpartum was 12.2605 cm2, while the average area of the hiatus in the pelvic floor ultrasound examination in the experimental group 42 days postpartum was 10.788 cm2; the average area of hiatus in the pelvic floor ultrasound examination in the control group at 3 months postpartum was 11.4805 cm2, and the average area of hiatus in the pelvic floor ultrasound examination in the experimental group at 3 months postpartum was 8.9475 cm2. To sum up, yoga had a very significant improvement on the physical indicators and mental health of postpartum women.

STEREO VISION INFORMATION SYSTEM USING MEDIAN THEOREM AND ATTITUDE COMPENSATION WITH NONLINEAR DIFFERENTIAL EQUATIONS

Using computer vision technology to obtain and analyze biomechanical information is an important research direction in recent years. However, the linear model in the computer vision system cannot accurately describe the geometric relationship of the camera imaging, so it is difficult to realize human posture recognition in high-precision mechanics information. Therefore, how to improve the recognition accuracy is very important. In this paper, we apply nonlinear differential equations to stereo computer vision (SCV) information systems. And based on the median theorem, a nonlinear posture recognition and error compensation algorithm based on BP neural network is proposed to reduce the recognition error. The test set uses the Leeds Motion Pose (LSP) dataset to verify the performance of the algorithm. Experimental results show that the compensated median filter of BP neural network can eliminate glitches in attitude data. Superimposing the output attitude error compensation value with the attitude estimation value can greatly reduce the root-mean-square error of the attitude angle. The result of gesture recognition is closer to reality. Compared with traditional algorithms, the cyclomatic complexity of the proposed BP neural network algorithm has a much lower growth rate in high-order calculations, which indicates that the proposed BP neural network algorithm is more concise and scalable.

SEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups.

Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal was measured using an armband sensor with the electrode shift. An artificial neural network classifier was trained using 21 feature vectors for seven different posture groups. The inter-session and inter-feature Pearson correlation coefficients (PCCs) were calculated. The results indicate that the classification performance improved with the number of training sessions of the electrode shift. The number of sessions necessary for efficient training was four, and the feature vectors with a high inter-session PCC (r > 0.7) exhibited high classification accuracy. Similarities between postures in a posture group decreased the classification accuracy. Our results indicate that the classification accuracy could be improved with the addition of more electrode shift training sessions and that the PCC is useful for selecting the feature vector. Furthermore, hand posture selection was as important as feature vector selection. These findings will help in optimizing the sEMG-based pattern recognition algorithm more easily and quickly.

Analysis of physical health risk dynamic evaluation system based on sports network technology

The people are the pillar of national construction and the driving force of social sustainable development. Therefore, people must develop morality, intelligence and body in an all-round way. The physical health risk assessment is of great significance to the improvement of the physical health of the public. Based on the sports network technology, this paper improves the traditional human posture recognition algorithm, and combines machine learning to construct the human body dynamic feature recognition method. Moreover, this paper combines actual needs to construct a physical health risk dynamic evaluation system based on sports network technology, which can recognize sports through pose recognition and combine dynamic monitoring technology to detect daily diet and living habits. In addition, this paper constructs the network structure of this paper system and designs its functional modules according to actual needs. Finally, this paper designs experiments to verify the performance of the model constructed in this paper. The research shows that the system constructed in this paper meets actual needs and can provide theoretical references for subsequent related research.

Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System.

As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy.

Study on forearm swing recognition algorithms to drive the underwater power‐assisted device of frogman

AbstractThe frogmen have a great demand for the underwater power‐assisted device (UPaD) because of their poor working conditions and complex missions. Although the wearable power‐assisted device on land has been diversified, the development of UPaDs is relatively slow. Meanwhile, the most of current small frogman transport equipment is needed to be manipulated by the frogmen with their hands with the aim of changing their moving speed or posture, which increases the difficulty of frogmen executing their tasks. The major contribution of this paper is to develop a method that frees the hands and realizes power assisting at the same time for frogmen, which can be applied to drive the UPaDs of frogmen. Two posture recognition algorithms based on the Human–Computer Interface have been promoted. The algorithms can recognize the forearm swings of the wearer of UPaD, calculate the frequency of the wearer's forearm swing, and adjust the moving speed of the wearer's UPaD according to the wearer's forearm swing frequency. Moreover, we evaluate the performance of two algorithms recognizing forearm swings underwater from four different subjects. Meanwhile, one of the two algorithms is improved and evaluated by experiments from the previous four subjects. The results of accuracy of two algorithms recognizing the wearer's forearm swing are more than 90%. The performance of the speed regulating system of UPaD based on the designed two algorithms is also acceptable.

Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image.

Bad sitting posture is harmful to human health. Intelligent sitting posture recognition algorithm can remind people to correct their sitting posture. In this paper, a sitting pressure image acquisition system was designed. With the system, we innovatively proposed a hip positioning algorithm based on hip templates. The average deviation of the algorithm for hip positioning is 1.306 pixels (the equivalent distance is 1.50 cm), and the proportion of the maximum positioning deviation less than three pixels is 94.1%. Statistics show that the algorithm works relatively well for different subjects. At the same time, the algorithm can not only effectively locate the hip position with a small rotation angle (0°–15°), but also has certain adaptability to the sitting posture with a medium rotation angle (15°–30°) or a large rotation angle (30°–45°). Using the hip positioning algorithm, the regional pressure values of the left hip, right hip and caudal vertebrae are effectively extracted as the features, and support vector machine (SVM) with polynomial kernel is used to classify the four types of sitting postures, with a classification accuracy of up to 89.6%.

Human body posture recognition algorithm for still images

Aiming at the low accuracy and poor robustness of the current algorithm based on manual features, this study proposed a posture recognition method combining joint point information with convolutional neural network. The deformable convolution is used in the proposed method to improve the stacked hourglass model, so that it can extract the position of the human joint point accurately. At the same time, the convolutional neural network structure is designed to analyse the position information and confidence of the joint point autonomously, and extract the intrinsic link of the joint point of the human body. Finally, the softmax classifier is used to determine the pose category. Experimental verification has been carried out on the Willow data set. Moreover, the recognition accuracy demonstrates the effectiveness and superiority of the improved method.

Classification accuracy of basketball simulation training system based on sensor fusion and Bayesian algorithm

As a pattern recognition application direction, human body posture recognition provides decision-making basis for human body behavior pattern analysis of human-computer intelligent interactive control. Therefore, in a complete human-computer intelligent interaction system, human body posture recognition is a necessary link that can complete the human body’s behavioral characterization and make humanized decision-making. This paper studies the athlete’s posture recognition algorithm based on multi-sensor method and completes the whole process from data acquisition to data processing and model algorithm construction and verification. Moreover, this paper designs experiments to verify the model’s recognition results for athletes, and discusses the results, and analyzes the advantages and disadvantages of the model in this experiment. In addition, this study takes basketball action as an example to take identification analysis. The results show that the proposed method has certain practical effects and can provide theoretical reference for subsequent related research.

Personalized Robot Assistant for Support in Dressing

Robot-assisted dressing is performed in close physical interaction with users who may have a wide range of physical characteristics and abilities. Design of user adaptive and personalized robots in this context is still indicating limited, or no consideration, of specific user-related issues. This paper describes the development of a multimodal robotic system for a specific dressing scenario—putting on a shoe, where users’ personalized inputs contribute to a much improved task success rate. We have developed: 1) user tracking, gesture recognition, and posture recognition algorithms relying on images provided by a depth camera; 2) a shoe recognition algorithm from RGB and depth images; and 3) speech recognition and text-to-speech algorithms implemented to allow verbal interaction between the robot and user. The interaction is further enhanced by calibrated recognition of the users’ pointing gestures and adjusted robot’s shoe delivery position. A series of shoe fitting experiments have been performed on two groups of users, with and without previous robot personalization, to assess how it affects the interaction performance. Our results show that the shoe fitting task with the personalized robot is completed in shorter time, with a smaller number of user commands, and reduced workload.

Warhead fragment position measurement method by using two light filed cameras

AbstractThe warhead fragments formed by projectile proximity explosion are easily occluded and coincided at the same time, it is very difficult to identify the spatial position of each warhead fragment, which will affect the analysis and research of the launching performance of weapons. To obtain dynamic parameter of each warhead fragment, this article proposes a new measurement method by using two light field cameras with micro‐lens array imaging, establishes the spatial position calculation model of warhead fragment, researches the spatial position recognition algorithm of warhead fragment, gives the calculation function of the depth value of the warhead fragment based on digital refocusing principle of light field camera. According to the depth information of the warhead fragment in the optical imaging region, sets up the recognition model and provides the processing algorithm of multiple warhead fragments occlusion and coincidence. In accordance with the spatial position calculation function of warhead fragment, utilizes the differential method to analyze the testing error. Through simulation and experimental analysis, the results of simulation and contrastive testing show that the proposed method can effectively recognize and distinguish the occlusive warhead fragments, which makes up for the shortcomings of the traditional binocular vision testing method.