ADHD Prediction via Time Series Ensemble fed Driving Simulator Data

Grethlein, David; Sladek, Aleksanteri; Ontañón, Santiago

doi:10.32473/flairs.v34i1.128531

Cited by 2 publications

(4 citation statements)

References 10 publications

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“…Long-term driving behavior is captured from features from long-term driving data (segment), while short-term driving behavior is captured from features from short-term driving data (segment). This second hypothesis is similar to the hypothesis in [25] in that only a few subintervals of time-series driving data are indicative of the class of drivers with ADHD. The detailed method of various durations segmentation is described in Section IV-C.…”

Section: B Segmentation With Various Durationssupporting

confidence: 78%

“…Wallace et al [24] classified drivers as those with Lewy body dementia, Alzheimer's type dementia, and healthy controls. Grethlein et al [25] [26] estimated drivers with attention deficit hyperactivity disorder (ADHD) from driving simulator data. These studies were similar to this study, but they used a driving simulation system to collect driving data.…”

Section: Driver Characteristics Estimationmentioning

confidence: 99%

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Estimating Driver Personality Traits From On-Road Driving Data

Kimura,

Tanaka,

Yoshihara

et al. 2023

IEEE Access

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This paper focuses on the estimation of a driver's psychological characteristics using driving data for driving assistance systems. Driving assistance systems that support drivers by adapting individual psychological characteristics can provide appropriate feedback and prevent traffic accidents. As a first step toward implementing such adaptive assistance systems, this research aims to develop a model to estimate drivers' psychological characteristics, such as cognitive function, psychological driving style, and workload sensitivity, from on-road driving behavioral data using machine learning and deep learning techniques. We also investigated the relationship between driving behavior and various cognitive functions, including the Trail Making Test (TMT) and Useful Field of View (UFOV) test, through regression modeling. The proposed method focuses on road type information and captures various durations of time-series data observed from driving behaviors. First, we segment the driving time-series data into two road types, namely, arterial roads and intersections, to consider driving situations. Second, we further segment data into many sequences of various durations. Third, statistics are calculated from each sequence. Finally, these statistics are used as input features of machine learning models to estimate psychological characteristics. The experimental results show that our model can estimate a driver's cognitive function, namely, the TMT (B) and UFOV test scores, with Pearson correlation coefficients r of 0.579 and 0.708, respectively. Some characteristics, such as psychological driving style and workload sensitivity, are estimated with high accuracy, but whether various duration segmentation improves accuracy depends on the characteristics, and it is not effective for all characteristics. Additionally, we reveal important sensor and road types for the estimation of cognitive function.

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Section: B Segmentation With Various Durationssupporting

confidence: 78%

Section: Driver Characteristics Estimationmentioning

confidence: 99%

Estimating Driver Personality Traits From On-Road Driving Data

Kimura,

Tanaka,

Yoshihara

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

Section: Segmentation With Various Durationssupporting

confidence: 78%

“…Wallace et al [24] classified drivers as those with Lewy body dementia, Alzheimer's type dementia, and healthy controls. Grethlein et al [25] [26] predicted drivers with attention deficit hyperactivity disorder (ADHD) from driving simulator data. These studies are similar to this study, but they used a driving simulation system to collect driving data.…”

Section: Driver Characteristics Estimationmentioning

confidence: 99%

Psychological Characteristics Estimation from On-Road Driving Data

Kimura,

Tanaka,

Yoshihara

et al. 2022

Social Computing and Social Media: Design, User Experience and Impact

View full text Add to dashboard Cite

Driving assistance systems that support drivers by adapting individual psychological characteristics can provide appropriate feedback and prevent traffic accidents. As a first step toward implementing such adaptive assistance systems, this research aims to develop a model to estimate drivers' psychological characteristics, such as cognitive function, psychological driving style, and workload sensitivity, from on-road driving behavioral data using machine learning and deep learning techniques. We also investigated the relationship between driving behavior and various cognitive functions including the Trail Making test and Useful Field of View test through regression modeling. The proposed method focuses on road type information and captures various durations of time-series data observed from driving behaviors. First, we segment the driving time-series data into two road types, namely, arterial roads and intersections, to consider driving situations. Second, we further segment data into many sequences of various durations. Third, statistics are calculated from each sequence. Finally, these statistics are used as input features of machine learning models to predict psychological characteristics. The experimental results show that our model can predict a driver's cognitive function, namely, the Trail Making Test version B and Useful Field of View test scores, with Pearson correlation coefficients r of 0.579 and 0.557, respectively. Some characteristics, such as psychological driving style and workload sensitivity, are predicted with high accuracy, but whether various duration segmentation improves accuracy depends on the characteristics, and it is not effective for all characteristics. Additionally, we reveal important sensor and road types for the estimation of cognitive function.

show abstract

ADHD Prediction via Time Series Ensemble fed Driving Simulator Data

Cited by 2 publications

References 10 publications

Estimating Driver Personality Traits From On-Road Driving Data

Estimating Driver Personality Traits From On-Road Driving Data

Psychological Characteristics Estimation from On-Road Driving Data

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