scholarly journals Human Motion Tracking Using 3D Image Features with a Long Short-Term Memory Mechanism Model—An Example of Forward Reaching

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 292
Author(s):  
Kai-Yu Chen ◽  
Li-Wei Chou ◽  
Hui-Min Lee ◽  
Shuenn-Tsong Young ◽  
Cheng-Hung Lin ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Short Term Memory ◽  
Human Motion ◽  
Image Features ◽  
3D Image ◽  
Image System ◽  
Short Term ◽  
Human Motion Tracking ◽  
Mechanism Model ◽  
Long Short Term Memory

Human motion tracking is widely applied to rehabilitation tasks, and inertial measurement unit (IMU) sensors are a well-known approach for recording motion behavior. IMU sensors can provide accurate information regarding three-dimensional (3D) human motion. However, IMU sensors must be attached to the body, which can be inconvenient or uncomfortable for users. To alleviate this issue, a visual-based tracking system from two-dimensional (2D) RGB images has been studied extensively in recent years and proven to have a suitable performance for human motion tracking. However, the 2D image system has its limitations. Specifically, human motion consists of spatial changes, and the 3D motion features predicted from the 2D images have limitations. In this study, we propose a deep learning (DL) human motion tracking technology using 3D image features with a deep bidirectional long short-term memory (DBLSTM) mechanism model. The experimental results show that, compared with the traditional 2D image system, the proposed system provides improved human motion tracking ability with RMSE in acceleration less than 0.5 (m/s2) X, Y, and Z directions. These findings suggest that the proposed model is a viable approach for future human motion tracking applications.

scholarly journals Early Warning of Internal Leakage in Heat Exchanger Network Based on Dynamic Mechanism Model and Long Short-Term Memory Method

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 378
Author(s):  
Wende Tian ◽  
Nan Liu ◽  
Dongwu Sui ◽  
Zhe Cui ◽  
Zijian Liu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Heat Exchanger ◽  
Early Warning ◽  
Short Term Memory ◽  
Short Term ◽  
Heat Exchanger Network ◽  
Term Memory ◽  
Organic Matters ◽  
Mechanism Model ◽  
Long Short Term Memory

In the process of butadiene rubber production, internal leakage occurs in heat exchangers due to excessive pressure difference. It leads to the considerable flow of organic matters into the circulating water system. Since these organic matters are volatile and prone to explode in the cold water tower, internal leakage is potentially dangerous for the enterprise. To prevent this phenomenon, a novel intelligent early warning and risk assessment method (DYN-EW-QRA) is proposed in this paper by combining dynamic simulations (DYN), long short-term memory (LSTM), and quantitative risk assessment (QRA). First, an original internal leakage mechanism model of a heat exchanger network is designed and simulated by DYN to obtain datasets. Second, the potential relationships between variables that have a direct impact on the hazards of the accident are deeply learned by LSTM to predict the internal leakage trends. Finally, the QRA method is used to analyze the range and destructive power of potential hazards. The results show that DYN-EW-QRA method has excellent performance.

scholarly journals A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096870
Author(s):  
Chenlei Xie ◽  
Daqing Wang ◽  
Haifeng Wu ◽  
Lifu Gao
Keyword(s):  
Neural Network ◽  
Knee Joint ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Human Motion ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Power Assistance

With the growth of the number of elderly and disabled with motor dysfunction, the demand for assisted exercise is increasing. Wearable power assistance robots are developed to provide athletic ability of limbs for the elderly or the disabled who have weakened limbs to better self-care ability. Existing wearable power-assisted robots generally use surface electromyography (sEMG) to obtain effective human motion intentions. Due to the characteristics of sEMG signals, it is limited in many applications. To solve the above problems, we design a long short-term memory (LSTM) neural network model based on human mechanomyography (MMG) signals to estimate the motion acceleration of knee joint. The acceleration can be further calculated by the torque required for movement control of the wearable power assistance robots for the lower limb. We detect MMG signals on the clothed thigh, extract features of the MMG signals, and then, use principal component analysis to reduce the features’ dimensions. Finally, the dimension-reduced features are inputted into the LSTM neural network model in time series for estimating the acceleration. The experimental results show that the average correlation coefficient ( R) is 94.48 ± 1.91% for the estimation of acceleration in the process of continuously performing under approximately π/4 rad/s. This approach can be applied in the practical applications of wearable field.

scholarly journals Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues

2020 ◽  
Vol 10 (24) ◽  
pp. 8931
Author(s):  
Mohamad M. Al Rahhal ◽  
Yakoub Bazi ◽  
Taghreed Abdullah ◽  
Mohamed L. Mekhalfi ◽  
Mansour Zuair
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Short Term Memory ◽  
Image Features ◽  
Single Image ◽  
Short Term ◽  
Textual Description ◽  
Textual Data ◽  
Long Short Term Memory ◽  
Lstm Network

Compared to image-image retrieval, text-image retrieval has been less investigated in the remote sensing community, possibly because of the complexity of appropriately tying textual data to respective visual representations. Moreover, a single image may be described via multiple sentences according to the perception of the human labeler and the structure/body of the language they use, which magnifies the complexity even further. In this paper, we propose an unsupervised method for text-image retrieval in remote sensing imagery. In the method, image representation is obtained via visual Big Transfer (BiT) Models, while textual descriptions are encoded via a bidirectional Long Short-Term Memory (Bi-LSTM) network. The training of the proposed retrieval architecture is optimized using an unsupervised embedding loss, which aims to make the features of an image closest to its corresponding textual description and different from other image features and vise-versa. To demonstrate the performance of the proposed architecture, experiments are performed on two datasets, obtaining plausible text/image retrieval outcomes.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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