Dangerous Driving Prediction Model based on Long Short-term Memory Network with Dynamic Weighted Moving Average of Heart-Rate Variability

Tsai, Cheng-Yu; Cheong, He-in; Houghton, Robert; Majumdar, Arnab; Liu, Wen‐Te; Lee, Kang‐Yun; Wu, Chia-Chang; Liu, Yi-Shin

doi:10.1109/icetas51660.2020.9484319

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…The prediction and voting system, based on the complete ECG waveform and the LSTM model with 192 data points respectively on the left and right sides of the R peak, had an accuracy of 75.5%. Compared with the 70.6% accuracy of the prediction and voting system of the SVM model, as well as [ 18 , 43 ] the PT algorithm used to find the positions of the PQRST points for the signal, this result was more accurate.…”

Section: Discussionmentioning

confidence: 99%

“…One method is to use the PT algorithm to find the positions of the PQRST points of the signal so as to cut out the complete wave [ 35 ]. The other method is to use the R peak, which is the highest point of each waveform, as the reference point for segmentation [ 18 ]. The ECG measurement App of the Apple Watch S6 used in this study could fixedly sample 512 data points per second [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…Wearable sensor data can also be combined with deep learning prediction, and are used further for related applications. For example, Pathinarupothi et al [ 16 ] used wearable sensor data to predict sleep apnea (with an accuracy of 99%), Zhou et al [ 17 ] used it to predict the learning state of students (with an accuracy of 74%), and Tsai et al [ 18 ] used it to predict dangerous driving behavior (with an accuracy of more than 80%). Mirza et al [ 19 ] predicted characteristics of disease infection with an accuracy of 68%, which is higher than that in the past.…”

Section: Introductionmentioning

confidence: 99%

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Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers

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Chung

2022

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In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been actively developing online channels where precision marketing has become the focus. This study proposed using the electrocardiography (ECG) recorded by wearable devices (e.g., smartwatches) to judge purchase intentions through deep learning. The method of this study included a long short-term memory (LSTM) model supplemented by collective decisions. The experiment was divided into two stages. The first stage aimed to find the regularity of the ECG and verify the research by repeated measurement of a small number of subjects. A total of 201 ECGs were collected for deep learning, and the results showed that the accuracy rate of predicting purchase intention was 75.5%. Then, incremental learning was adopted to carry out the second stage of the experiment. In addition to adding subjects, it also filtered five different frequency ranges. This study employed the data augmentation method and used 480 ECGs for training, and the final accuracy rate reached 82.1%. This study could encourage online marketers to cooperate with health management companies with cross-domain big data analysis to further improve the accuracy of precision marketing.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers

Chang

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Chung

2022

Sensors

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A Framework for Selecting and Assessing Wearable Sensors Deployed in Safety Critical Scenarios

Houghton,

Martinetti,

Majumdar

2024

Sensors

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Wearable sensors for psychophysiological monitoring are becoming increasingly mainstream in safety critical contexts. They offer a novel solution to capturing sub-optimal states and can help identify when workers in safety critical environments are suffering from states such as fatigue and stress. However, sensors can differ widely in their application, design, usability, and measurement and there is a lack of guidance on what should be prioritized or considered when selecting a sensor. The paper aims to highlight which concepts are important when creating or selecting a device regarding the optimization of both measurement and usability. Additionally, the paper discusses how design choices can enhance both the usability and measurement capabilities of wearable sensors. The hopes are that this paper will provide researchers and practitioners in human factors and related fields with a framework to help guide them in building and selecting wearable sensors that are well suited for deployment in safety critical contexts.

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Dangerous Driving Prediction Model based on Long Short-term Memory Network with Dynamic Weighted Moving Average of Heart-Rate Variability

Cited by 2 publications

References 27 publications

Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers

Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers

A Framework for Selecting and Assessing Wearable Sensors Deployed in Safety Critical Scenarios

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