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

Kim, In Soo; Hong, Ellen J.; Chung, Kyungyong; Park, Roy C.

doi:10.3390/app10082956

Cited by 23 publications

(21 citation statements)

References 40 publications

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“…In some other cases, there want to be able to predict drivers' behavior; to make this possible CRNNs are used. Kim et al [72] proposed a line-segment feature analysis-convolutional recurrent neural network (LFA-CRNN) to analyze drivers' facial expressions indicating pain to prevent automobile accidents and respond to emergency health-risk situations. The LFA-CRNN showed an accuracy of 97.4% in contrast with 98.21% and 97.4% of CRNN and AlexNet.…”

Section: Human Sentiment Analysismentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Medina¹,

Espitia²,

Silva³

et al. 2021

Preprint

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Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.

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Section: Human Sentiment Analysismentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Medina¹,

Espitia²,

Silva³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the healthcare field, time is a significant factor to treat and prevent diseases [25]. Accordingly, it is important to apply sequential pattern mining to healthcare data in order to analyze the development order of diseases and main treatment paths [4,5,26].…”

Section: B Sequential Pattern Mining For Healthcarementioning

confidence: 99%

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

2020

Self Cite

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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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“…It predicts the user's health status by learning images generated in medical processes such as CT, MRI, and X-ray. Neural networks using various techniques are used based on CNN, which has high efficiency in image data [22].…”

Section: B Deep Learning-based Smart Healthcare Modelmentioning

confidence: 99%

Hybrid Multi-Modal Deep Learning using Collaborative Concat Layer in Health Bigdata

Kim

Chung

2020

IEEE Access

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A health model based on data has various missing values depending on the user situation, and the accuracy of a health model requiring variables that the user cannot collect appears low. A deep learning health model is fitted by learning weights to increase accuracy. This implies that additional learning is required when the environment changes, such as when new variables are added or deleted depending on the user situation. In the process of applying a deep-learning-based health model to the user situation, accuracy may be degraded if learning is omitted. In this paper, we propose hybrid multimodal deep learning using a collaborative concat layer in health big data. The proposed method uses a machine learning technique to alleviate the issue caused by the change in the data observation range according to a change in the user situation, and occurring in multimodal health deep learning. It is a layer composed of the connection, input, and output of the model of the collaborative node (CN). A CN is a node that predicts absent variables through filtering using the similarity of input values. With CN, a collaborative concat layer (CCL) that handles missing values from the input of the health model can be configured, and the issue related to missing values occurring in the health model can be resolved. With the proposed CCL, it is possible to reuse existing models or construct new models through the concatenation of several single-modal deep learning models. By evaluating the effect on the input and output of the model according to the structural position of the CCL, various networks can be configured, and the performance of the single-modal model can be maintained. In particular, the accuracy of a deep learning model is more stable when the CCL is used, suggesting the experiment progress based on the assumption that a specific variable is absent depending on the user situation.

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

Cited by 23 publications

References 40 publications

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

Hybrid Multi-Modal Deep Learning using Collaborative Concat Layer in Health Bigdata

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