WisDriver:A WiFi and Smartphone Sensing System for Safely Driving

Li, Qingpei; Li, Meng; Xia, Ke; Kong, Weixing; Lei, Yang; Tang, Zhanyong; Liu, Chen; Fang, Dingyi

doi:10.1109/pacrim47961.2019.8985091

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…Li et al [86] combine different data from smartphones and WiFi signals to identify 15 risky driving actions (e.g., snoring, head turned, the use of phones, hands on the steering wheel). The system called WisDriver classifies risky driving actions into three types: head movement, arm movement, and body movement.…”

Section: Author-centric Analysis: Summary Of Individual Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

Papatheocharous,

Kaiser,

Moser

et al. 2023

Sensors

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Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents. With the increasing number of sensors available in vehicles, there is an abundance of data available to monitor driver behaviour, but it has only been available to vehicle manufacturers and, to a limited extent, through proprietary solutions. Recently, research and practice have shifted the paradigm to the use of smartphones for driver monitoring and have fuelled efforts to support driving safety. This systematic review paper extends a preliminary, previously carried out author-centric literature review on smartphone-based driver monitoring approaches using snowballing search methods to illustrate the opportunities in using smartphones for driver distraction detection. Specifically, the paper reviews smartphone-based approaches to distracted driving behaviour detection, the smartphone sensors and detection methods applied, and the results obtained.

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Section: Author-centric Analysis: Summary Of Individual Resultsmentioning

confidence: 99%

“…The analysis methods used include machine learning models [66,68,[77][78][79]84,86,90,103,105,107,[109][110][111][112],…”

Section: Aggregated Results: Summary Of Tangible Resultsmentioning

confidence: 99%

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

Papatheocharous,

Kaiser,

Moser

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Since then, the channel state information from physical layer has been explored in activity recognition [ 7 ], indoor location [ 8 ], gesture recognition [ 9 , 10 ], and user identification [ 11 ]. The WiFi signals can be used to perform fall detection [ 12 ] and detect risky driving behavior [ 13 ]. Many studies focus on how to process CSI data and most common solutions are based on machine learning algorithms, such as support vector machine(SVM) [ 14 ] and convolutional neural network(CNN) [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

Yin

Tang

2021

Sensors

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Currently, there are various works presented in the literature regarding the activity recognition based on WiFi. We observe that existing public data sets do not have enough data. In this work, we present a data augmentation method called window slicing. By slicing the original data, we get multiple samples for one raw datum. As a result, the size of the data set can be increased. On the basis of the experiments performed on a public data set and our collected data set, we observe that the proposed method assists in improving the results. It is notable that, on the public data set, the activity recognition accuracy improves from 88.13% to 97.12%. Similarly, the recognition accuracy is also improved for the data set collected in this work. Although the proposed method is simple, it effectively enhances the recognition accuracy. It is a general channel state information (CSI) data augmentation method. In addition, the proposed method demonstrates good interpretability.

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WisDriver:A WiFi and Smartphone Sensing System for Safely Driving

Cited by 2 publications

References 18 publications

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

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