Establishing an Intelligent Emotion Analysis System for Long-Term Care Application Based on LabVIEW

Abstract

In this study, the authors implemented an intelligent long-term care system based on deep learning techniques, using an AI model that can be integrated with the Lab’s Virtual Instrumentation Engineering Workbench (LabVIEW) application for sentiment analysis. The input data collected is a database of numerous facial features and environmental variables that have been processed and analyzed; the output decisions are the corresponding controls for sentiment analysis and prediction. Convolutional neural network (CNN) is used to deal with the complex process of deep learning. After the convolutional layer simplifies the processing of the image matrix, the results are computed by the fully connected layer. Furthermore, the Multilayer Perceptron (MLP) model embedded in LabVIEW is constructed for numerical transformation, analysis, and predictive control; it predicts the corresponding control of emotional and environmental variables. Moreover, LabVIEW is used to design sensor components, data displays, and control interfaces. Remote sensing and control is achieved by using LabVIEW’s built-in web publishing tools.

An experimental study of intensity‐phase characterization of femtosecond laser pulses propagated through a polymethyl methacrylate

“…Multiple spectra are acquired at several steps of equal intervals of the motorized translation stage. 21 The obtained data contains 50 delay points with a step size of 1.8 μm; data is arranged in a 50 ×50 matrix and is used to retrieve spectral and temporal profiles of the pulse using FROG retrieval software (Femtosoft, Inc.). 22 Figure 1B shows the XFROG setup; the basic setup and data acquisition scheme is the same as FROG; only the PMMA sample is introduced in the fixed arm of the XFROG.…”

“…Using a USB‐type compact spectrometer (CCS200/M; Thorlabs, Inc.), the output signal is spectrally resolved and recorded as a function of the delay between the two pulses. Multiple spectra are acquired at several steps of equal intervals of the motorized translation stage 21 . The obtained data contains 50 delay points with a step size of 1.8 μm; data is arranged in a 50 ×50 matrix and is used to retrieve spectral and temporal profiles of the pulse using FROG retrieval software (Femtosoft, Inc.) 22 .…”

Analysis model of college students' mental health based on online community topic mining and emotion analysis in novel coronavirus epidemic situation

“…Because of the large amount of Chinese corpus, it is impossible for the system to obtain all the text data at the same time, so the coverage of the training data in the system is very low, which requires the system to continuously update the training data. In English sentiment analysis system, word segmentation is not required for texts, but it is one of the necessary steps for Chinese text classification ( 19 ). The most important step in sentiment analysis is to choose the appropriate classification algorithm to construct a text classifier, which needs to be determined by combining the characteristics of data, algorithms, and text features.…”

Emotion Recognition in Usability Testing: A Framework for Improving Web Application UI Design

“…The most common applications of emotion recognition technologies are in the development of computer games to enhance the overall experience [9]; in education, when trying to improve the teaching and accessibility of the program, by taking into account the emotional state of the students [10]; in medicine to detect health problems earlier [11]; in advertising companies trying to understand what kind of product the respective market wants; or in customer service by providing the best possible service quality. Medjden et al investigated the automatic adaptation of the user interface depending on the multimodal emotion recognition system using the RGB-D sensor [12].…”