Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses
One of the main reasons for fatal accidents on the road is distracted driving. The continuous attention of an individual driver is a necessity for the task of driving. While driving, certain levels of distraction can cause drivers to lose their attention, which might lead to an accident. Thus, the n...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/2/503 |
| _version_ | 1798004140506349568 |
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| author | Omid Dehzangi Vikas Rajendra Mojtaba Taherisadr |
| author_facet | Omid Dehzangi Vikas Rajendra Mojtaba Taherisadr |
| author_sort | Omid Dehzangi |
| collection | DOAJ |
| description | One of the main reasons for fatal accidents on the road is distracted driving. The continuous attention of an individual driver is a necessity for the task of driving. While driving, certain levels of distraction can cause drivers to lose their attention, which might lead to an accident. Thus, the number of accidents can be reduced by early detection of distraction. Many studies have been conducted to automatically detect driver distraction. Although camera-based techniques have been successfully employed to characterize driver distraction, the risk of privacy violation is high. On the other hand, physiological signals have shown to be a privacy preserving and reliable indicator of driver state, while the acquisition technology might be intrusive to drivers in practical implementation. In this study, we investigate a continuous measure of phasic Galvanic Skin Responses (GSR) using a wristband wearable to identify distraction of drivers during a driving experiment on-the-road. We first decompose the raw GSR signal into its phasic and tonic components using Continuous Decomposition Analysis (CDA), and then the continuous phasic component containing relevant characteristics of the skin conductance signals is investigated for further analysis. We generated a high resolution spectro-temporal transformation of the GSR signals for non-distracted and distracted (calling and texting) scenarios to visualize the associated behavior of the decomposed phasic GSR signal in correlation with distracted scenarios. According to the spectrogram observations, we extract relevant spectral and temporal features to capture the patterns associated with the distracted scenarios at the physiological level. We then performed feature selection using support vector machine recursive feature elimination (SVM-RFE) in order to: (1) generate a rank of the distinguishing features among the subject population, and (2) create a reduced feature subset toward more efficient distraction identification on the edge at the generalization phase. We employed support vector machine (SVM) to generate the 10-fold cross validation (10-CV) identification performance measures. Our experimental results demonstrated cross-validation accuracy of 94.81% using all the features and the accuracy of 93.01% using reduced feature space. The SVM-RFE selected set of features generated a marginal decrease in accuracy while reducing the redundancy in the input feature space toward shorter response time necessary for early notification of distracted state of the driver. |
| first_indexed | 2024-04-11T12:18:53Z |
| format | Article |
| id | doaj.art-fd59d7b9384e471c9c5c1ef1c0240a68 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:18:53Z |
| publishDate | 2018-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fd59d7b9384e471c9c5c1ef1c0240a682022-12-22T04:24:10ZengMDPI AGSensors1424-82202018-02-0118250310.3390/s18020503s18020503Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin ResponsesOmid Dehzangi0Vikas Rajendra1Mojtaba Taherisadr2Computer and Information Science Department, University of Michigan-Dearborn, Dearborn, MI 48128, USAComputer and Information Science Department, University of Michigan-Dearborn, Dearborn, MI 48128, USAComputer and Information Science Department, University of Michigan-Dearborn, Dearborn, MI 48128, USAOne of the main reasons for fatal accidents on the road is distracted driving. The continuous attention of an individual driver is a necessity for the task of driving. While driving, certain levels of distraction can cause drivers to lose their attention, which might lead to an accident. Thus, the number of accidents can be reduced by early detection of distraction. Many studies have been conducted to automatically detect driver distraction. Although camera-based techniques have been successfully employed to characterize driver distraction, the risk of privacy violation is high. On the other hand, physiological signals have shown to be a privacy preserving and reliable indicator of driver state, while the acquisition technology might be intrusive to drivers in practical implementation. In this study, we investigate a continuous measure of phasic Galvanic Skin Responses (GSR) using a wristband wearable to identify distraction of drivers during a driving experiment on-the-road. We first decompose the raw GSR signal into its phasic and tonic components using Continuous Decomposition Analysis (CDA), and then the continuous phasic component containing relevant characteristics of the skin conductance signals is investigated for further analysis. We generated a high resolution spectro-temporal transformation of the GSR signals for non-distracted and distracted (calling and texting) scenarios to visualize the associated behavior of the decomposed phasic GSR signal in correlation with distracted scenarios. According to the spectrogram observations, we extract relevant spectral and temporal features to capture the patterns associated with the distracted scenarios at the physiological level. We then performed feature selection using support vector machine recursive feature elimination (SVM-RFE) in order to: (1) generate a rank of the distinguishing features among the subject population, and (2) create a reduced feature subset toward more efficient distraction identification on the edge at the generalization phase. We employed support vector machine (SVM) to generate the 10-fold cross validation (10-CV) identification performance measures. Our experimental results demonstrated cross-validation accuracy of 94.81% using all the features and the accuracy of 93.01% using reduced feature space. The SVM-RFE selected set of features generated a marginal decrease in accuracy while reducing the redundancy in the input feature space toward shorter response time necessary for early notification of distracted state of the driver.http://www.mdpi.com/1424-8220/18/2/503driver distractiongalvanic skin responseskin conductancecontinuous decomposition analysisspectro-temporal characterizationSVM-RFE feature selection |
| spellingShingle | Omid Dehzangi Vikas Rajendra Mojtaba Taherisadr Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses Sensors driver distraction galvanic skin response skin conductance continuous decomposition analysis spectro-temporal characterization SVM-RFE feature selection |
| title | Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses |
| title_full | Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses |
| title_fullStr | Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses |
| title_full_unstemmed | Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses |
| title_short | Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses |
| title_sort | wearable driver distraction identification on the road via continuous decomposition of galvanic skin responses |
| topic | driver distraction galvanic skin response skin conductance continuous decomposition analysis spectro-temporal characterization SVM-RFE feature selection |
| url | http://www.mdpi.com/1424-8220/18/2/503 |
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