scholarly journals ARMA-Based Segmentation of Human Limb Motion Sequences

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5577
Author(s):  
Feng Mei ◽  
Qian Hu ◽  
Changxuan Yang ◽  
Lingfeng Liu
Keyword(s):  
Clustering Algorithm ◽  
Back Propagation ◽  
Model Fitting ◽  
Arma Model ◽  
Human Motion ◽  
Structural Representation ◽  
Unsupervised Segmentation ◽  
Limb Bone ◽  
Computation Efficiency ◽  
Human Limb

With the development of human motion capture (MoCap) equipment and motion analysis technologies, MoCap systems have been widely applied in many fields, including biomedicine, computer vision, virtual reality, etc. With the rapid increase in MoCap data collection in different scenarios and applications, effective segmentation of MoCap data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb-bone partition angle body structural representation and autoregressive moving average (ARMA) model fitting. The collected MoCap data were converted into the angle sequence formed by the human limb-bone partition segment and the central spine segment. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements were also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and were labeled by manual segmentation. The proposed algorithm is compared with the principle component analysis (PCA), K-means clustering algorithm (K-means), and back propagation (BP) neural-network-based segmentation algorithms, which shows higher segmentation accuracy due to a more semantic description of human motions by limb-bone partition angles. The results highlight the efficiency and performance of the proposed algorithm, and reveals the potentials of this segmentation model on analyzing inter- and intra-motion sequence distinguishing.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

Diagnosis of infertility from adenomyosis and endometriosis through entroxon based intelligent water drop back propagation neural networks

2022 ◽  
pp. 1-9
Author(s):  
P. Vijayalakshmi ◽  
K. Muthumanickam ◽  
G. Karthik ◽  
S. Sakthivel
Keyword(s):  
Clustering Algorithm ◽  
Water Drop ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Uterine Wall ◽  
Health Issues ◽  
Testing Dataset ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
The Difference

Adenomyosis is an abnormality in the uterine wall of women that adversely affects their normal life style. If not treated properly, it may lead to severe health issues. The symptoms of adenomyosis are identified from MRI images. It is a gynaecological disease that may lead to infertility. The presence of red dots in the uterus is the major symptom of adenomyosis. The difference in the extent of these red dots extracted from MRI images shows how significant the deviation from normality is. Thus, we proposed an entroxon-based bio-inspired intelligent water drop back-propagation neural network (BIWDNN) model to discover the probability of infertility being caused by adenomyosis and endometriosis. First, vital features from the images are extracted and segmented, and then they are classified using the fuzzy C-means clustering algorithm. The extracted features are then attributed and compared with a normal person’s extracted attributes. The proposed BIWDNN model is evaluated using training and testing datasets and the predictions are estimated using the testing dataset. The proposed model produces an improved diagnostic precision rate on infertility.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

Classification of Brain MR Images Using Corpus Callosum Shape Measurements

2017 ◽  
pp. 1427-1436
Author(s):  
Gaurav Vivek Bhalerao ◽  
Niranjana Sampathila
Keyword(s):  
Neural Network ◽  
Corpus Callosum ◽  
Clustering Algorithm ◽  
Neurological Diseases ◽  
Back Propagation ◽  
Mr Images ◽  
Dementia Patients ◽  
Male Patients ◽  
The Brain

The corpus callosum is the largest white matter structure in the brain, which connects the two cerebral hemispheres and facilitates the inter-hemispheric communication. Abnormal anatomy of corpus callosum has been revealed for various brain related diseases. Being an important biomarker, Magnetic Resonance Imaging of the brain followed by corpus callosum segmentation and feature extraction has found to be important for the diagnosis of many neurological diseases. This paper focuses on classification of T1-weighted mid-sagittal MR images of brain for dementia patients. The corpus callosum is segmented using K-means clustering algorithm and corresponding shape based measurements are used as features. Based on these shape based measurements, a back-propagation neural network is trained separately for male and female dataset. The input data consists of 54 female and 31 male patients. This paper reports classification accuracy up to 92% for female patients and 94% for male patients using neural network classifier.

scholarly journals Target-oriented Gaussian beam migration using a modified ray tracing scheme

2019 ◽  
Vol 16 (6) ◽  
pp. 1301-1319 ◽  
Author(s):  
Rui Zhang ◽  
Jian-Ping Huang ◽  
Su-Bin Zhuang ◽  
Zhen-Chun Li
Keyword(s):  
Gaussian Beam ◽  
Ray Tracing ◽  
Large Scale ◽  
High Efficiency ◽  
Complex Structure ◽  
Back Propagation ◽  
Imaging Method ◽  
Computation Efficiency ◽  
Simple Implementation ◽  
Gaussian Beam Migration

Abstract For large-scale 3D seismic data, target-oriented reservoir imaging is more attractive than conventional full-volume migration, in terms of computation efficiency. Gaussian beam migration (GBM) is one of the most robust depth imaging method, which not only keeps the advantages of ray methods, such as high efficiency and flexibility, but also allows us to solve caustics and multipathing problems. But conventional Gaussian beam migration requires slant stack for prestack data, and ray tracing from beam center location to subsurface, which is not easy to be directly applied for target-oriented imaging. In this paper, we modify the conventional Gaussian beam migration scheme, by shooting rays from subsurface image points to receivers to implement wavefield back-propagation. This modification helps us to achieve a better subsurface illumination in complex structure and allows simple implementation for target reservoir imaging. Significantly, compared with the wavefield-based GBM, our method does not reconstruct the subsurface snapshots, which has higher efficiency. But the proposed method is not as efficient as the conventional Gaussian beam migration. Synthetic and field data examples demonstrate the validity and the target-oriented imaging capability of our method.

Wavelet-Based ARMA Model Application in Power Network

2011 ◽  
Vol 121-126 ◽  
pp. 1509-1513 ◽  
Author(s):  
Wei Wei ◽  
Hao Ma
Keyword(s):  
Model Fitting ◽  
Arma Model ◽  
Power Network ◽  
Model Application ◽  
Modeling Process

The theoretical properties of the ARMA model and the modeling process, then, the Shanghai power network and Shenzhen power network in China were established ARMA model and wavelet-based ARMA model fitting, prediction, and finally, to fit forecast The results were compared. It can be seen, combined with relatively good forecasting effect after wavelet.

scholarly journals R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1421 ◽  
Author(s):  
Jijun Zhao ◽  
Lishuang Liu ◽  
Zhongcheng Wei ◽  
Chunhua Zhang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Motion Detection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Research Area ◽  
Human Motion ◽  
Average Error ◽  
Area Of Interest ◽  
Human Motion Detection ◽  
Average Error Rate ◽  
Duration Estimation

As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively.

scholarly journals Inconsistency Calibrating Algorithms for Large Scale Piezoresistive Electronic Skin

Micromachines ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 162 ◽  
Author(s):  
Jinhua Ye ◽  
Zhengkang Lin ◽  
Jinyan You ◽  
Shuheng Huang ◽  
Haibin Wu
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Tactile Sensor ◽  
Human Robot Interaction ◽  
Detection Accuracy ◽  
Position Information ◽  
Detection Model ◽  
Electronic Skin ◽  
Calibration Methods

In the field of safety and communication of human-robot interaction (HRI), using large-scale electronic skin will be the tendency in the future. The force-sensitive piezoresistive material is the key for piezoresistive electronic skin. In this paper, a non-array large scale piezoresistive tactile sensor and its corresponding calibration methods were presented. Because of the creep inconsistency of large scale piezoresistive material, a creep tracking compensation method based on K-means clustering and fuzzy pattern recognition was proposed to improve the detection accuracy. With the compensated data, the inconsistency and nonlinearity of the sensor was calibrated. The calibration process was divided into two parts. The hierarchical clustering algorithm was utilized firstly to classify and fuse piezoresistive property of different regions over the whole sensor. Then, combining the position information, the force detection model was constructed by Back-Propagation (BP) neural network. At last, a novel flexible tactile sensor for detecting contact position and force was designed as an example and tested after being calibrated. The experimental results showed that the calibration methods proposed were effective in detecting force, and the detection accuracy was improved.

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