Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

Kang, JiSoo; Shin, Dong-Hoon; Baek, Jiwon; Chung, Kyungyong

doi:10.3390/app9204284

Cited by 15 publications

(11 citation statements)

References 29 publications

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“…For the comparison, sequential patterns are discovered from diabetes-health-data, with the uses of GSP, SPADE, and Prefixspan algorithms, and the proposed TSW_PrefixSpan algorithm. Based on the extracted sequential patterns, these algorithms are compared in terms of accuracy and F-measure based on precision and recall [42]. In comparison, about 63,000 diabetic patient data are divided into 70% training data and 30% test data.…”

Section: ) Overall Comparsion Using F-measurementioning

confidence: 99%

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

2020

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This study proposes the prefixSpan based pattern mining using time sliding weight from streaming data. To discover sequential patterns, it applies a time sliding weight to create a structure of projected DB Tree. For the time sliding weight, a time window is applied to the sequential data to calculate the label and support of the window. When a projected DB Tree is designed, the time weight calculated for each pattern is inserted in a table. At this time, the tree is updated by deleting the node whose time weight is less than the reference value. For this reason, whenever data is updated, the tree is sorted again. The reordering process removes the pattern of less influence by applying time weights. Therefore, it is possible to construct a projected DB Tree that can extract influential patterns. The performance of the proposed method is evaluated in three aspects. Firstly, the conventional PrefixSpan algorithm is compared with the proposed PrefixSpan algorithm based on time sliding weight in terms of the pattern generation time according to size of data. Secondly, the fitness of the proposed algorithm is evaluated through cross-validation. Thirdly, GSP, SPADE, and prefixSpan algorithms are compared with the proposed algorithm through F-measure. As a result of the evaluation, the proposed algorithm improves accuracy 75% more than PrefixSpan algorithm and the sequential pattern algorithms of GSP and SPADE. Regarding the comparison of F-measure based on precision and recall, the proposed one improves its performance about 83%.

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Section: ) Overall Comparsion Using F-measurementioning

confidence: 99%

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

2020

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“…In such cases, even after a driver's abnormal health status is detected, he or she might not be able to do something within the so-called "golden time" to address the problem. Although the mortality of patients with chronic diseases has decreased due to medical progress, it is still necessary to continuously manage and prepare for emergency situations [12,13]. To that end, services are being studied that alert friends, hospitals, police stations, etc., after detecting a driver at risk.…”

Section: Introductionmentioning

confidence: 99%

Driver Facial Expression Analysis Using LFA-CRNN-Based Feature Extraction for Health-Risk Decisions

et al. 2020

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As people communicate with each other, they use gestures and facial expressions as a means to convey and understand emotional state. Non-verbal means of communication are essential to understanding, based on external clues to a person’s emotional state. Recently, active studies have been conducted on the lifecare service of analyzing users’ facial expressions. Yet, rather than a service necessary for everyday life, the service is currently provided only for health care centers or certain medical institutions. It is necessary to conduct studies to prevent accidents that suddenly occur in everyday life and to cope with emergencies. Thus, we propose facial expression analysis using line-segment feature analysis-convolutional recurrent neural network (LFA-CRNN) feature extraction for health-risk assessments of drivers. The purpose of such an analysis is to manage and monitor patients with chronic diseases who are rapidly increasing in number. To prevent automobile accidents and to respond to emergency situations due to acute diseases, we propose a service that monitors a driver’s facial expressions to assess health risks and alert the driver to risk-related matters while driving. To identify health risks, deep learning technology is used to recognize expressions of pain and to determine if a person is in pain while driving. Since the amount of input-image data is large, analyzing facial expressions accurately is difficult for a process with limited resources while providing the service on a real-time basis. Accordingly, a line-segment feature analysis algorithm is proposed to reduce the amount of data, and the LFA-CRNN model was designed for this purpose. Through this model, the severity of a driver’s pain is classified into one of nine types. The LFA-CRNN model consists of one convolution layer that is reshaped and delivered into two bidirectional gated recurrent unit layers. Finally, biometric data are classified through softmax. In addition, to evaluate the performance of LFA-CRNN, the performance was compared through the CRNN and AlexNet Models based on the University of Northern British Columbia and McMaster University (UNBC-McMaster) database.

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“…A healthcare system needs to be expanded to the smart health service using life big data mining in order for chronic disease patients to care for themselves [ 2 ]. Therefore, it tries to develop to provide the health service for preventing recurrences of chronic diseases, such as diabetes, hypertension, dyslipidemia, cardiovascular disorders, and cerebrovascular disorders, and achieving early detection and care of stress and depression symptoms [ 3 , 4 ]. In addition, through the context recognition system, it is necessary to integrate the IoT based bio log big data individually obtained with a variety of health records and to analyze the data in an intelligent technique and provide customized information [ 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it tries to develop to provide the health service for preventing recurrences of chronic diseases, such as diabetes, hypertension, dyslipidemia, cardiovascular disorders, and cerebrovascular disorders, and achieving early detection and care of stress and depression symptoms [ 3 , 4 ]. In addition, through the context recognition system, it is necessary to integrate the IoT based bio log big data individually obtained with a variety of health records and to analyze the data in an intelligent technique and provide customized information [ 4 , 5 , 6 , 7 ]. Recently, active research has been conducted on the current neural network-based health model in which recurrent connections of sequences are effectively learned to recognize sequential context knowledge and extract causal relations.…”

Section: Introductionmentioning

confidence: 99%

A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

2020

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Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

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Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

Cited by 15 publications

References 29 publications

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

Driver Facial Expression Analysis Using LFA-CRNN-Based Feature Extraction for Health-Risk Decisions

A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

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