Shantanu Deshmukh

One of the most contributing factors to the accidents on the roadways is distracted driving. While in-vehicle, driver may get distracted by variety of ways such as talking on the cellphone, conversing with the accompanying passengers, texting while driving, etc. In order to reduce potential chances of road-accidents, it is highly essential to characterize and identify distracted situations in real-time. In this paper, we investigate Electrocardiogram (ECG) signals as the physiological measure… Show more

One of the most contributing factors to the accidents on the roadways is distracted driving. While in-vehicle, driver may get distracted by variety of ways such as talking on the cellphone, conversing with the accompanying passengers, texting while driving, etc. In order to reduce potential chances of road-accidents, it is highly essential to characterize and identify distracted situations in real-time. In this paper, we investigate Electrocardiogram (ECG) signals as the physiological measure to characterize driver distraction. We aim to provide an empirical guideline for accurate and in real-time analysis irrespective of the body physic. ECG-based driver distraction identification has significant advantages in practice such as being easy to capture, minimally intrusive, and reliable in biometric patterns. ECG dynamics encompass multiple descriptors that we examine in this investigation for efficient characterization of driver state toward real-time identification of distracted driving. In this effort, six drivers were actively participated in our naturalistic driving experiments, where the distraction is introduced by the cellphone conversation and the conversation with the passenger. Our study mainly focuses on the efficient characterization of distraction by localizing R-R interval based on temporal features as well as spectral features. In addition to this, we further investigated the real-time predictive ability of the extracted features through state of the art predictive algorithms. Our experimental results demonstrated ∼83% average predictive accuracy of driver distraction identification in near real-time. Show less

Driver Distraction is one of the main reasons behind the increasing number of fatalities on the road. In order to minimize the potential road disasters, it is essential to monitor and track the pre-requisites of driver distraction. While driving, the driver might get distracted in variety of ways such as talking on the cell phone, texting, or having a conversation with a passenger. In the recent years, extensive investigations are directed towards the problem of characterizing the impact of… Show more

Driver Distraction is one of the main reasons behind the increasing number of fatalities on the road. In order to minimize the potential road disasters, it is essential to monitor and track the pre-requisites of driver distraction. While driving, the driver might get distracted in variety of ways such as talking on the cell phone, texting, or having a conversation with a passenger. In the recent years, extensive investigations are directed towards the problem of characterizing the impact of secondary tasks while driving, predominantly using camera-based systems. However, camera-based systems incur major challenges such as privacy or latency in detection. Using physiological signals to identify distraction such as Electroencephalography (EEG) has been shown to accomplish more reliable detection. However, EEG- based detection systems necessitate intrusive implementation and complex signal processing. On the other hand, Electrocardiogram (ECG) is a reliable signal which can characterize the physiological changes consistently, with minimal intrusiveness, and at low cost. In this paper, we focus on ECG signal processing aspect with the aim of predicting driver distraction. Eight subjects aged 24 ± 45, actively participated in the naturalistic driving experiment where distraction was induced by: 1) phone conversation and 2) engaging an active conversation between the driver and the passenger. We present an effective frequency subBand analysis using Wavelet Packet Transform (WPT) to localize the impact of distracting elements. Due to high dimensionality of the WPT generated space, we then applied Linear Discriminant Analysis (LDA) for feature space dimensionality reduction; preserving discriminative capability of the predictive model. In order to further enhance the prediction ability of the system, we used kernel transformation in order to take into account non-linear interactions of the input feature space. Show less

Many of the fatalities involved on-road accidents are associated with driver distraction. In order to reduce the possible chances of road disasters, it is essential to characterize the pre-requisites of driver distraction. While driving, the driver might get distracted by several ways such as talking on the cell phone, texting, and having a conversation with the passenger. There has been extensive research conducted to estimate driver states in recent years particularly on camera and EEG-based… Show more

Many of the fatalities involved on-road accidents are associated with driver distraction. In order to reduce the possible chances of road disasters, it is essential to characterize the pre-requisites of driver distraction. While driving, the driver might get distracted by several ways such as talking on the cell phone, texting, and having a conversation with the passenger. There has been extensive research conducted to estimate driver states in recent years particularly on camera and EEG-based systems. However, camera-based systems face challenges such as privacy or latency in detection. On the other hand, Electroencephalography (EEG) based detection can accomplish more reliable detection. However, this technology requires an intrusive implementation. Electrocardiogram (ECG) is a reliable signal which can characterize the physiological changes consistently, with minimal intrusiveness, and at low cost. In this paper, we propose an ECG signal processing recipe with the aim of predicting driver distraction in real-time. Six drivers actively participated in the naturalistic driving experiment where distraction was induced by: 1) making a phone call and 2) having an active conversation between the driver and the passenger. We present an effective frequency subBand analysis using Wavelet Packet Transform (WPT). Due to high dimensionality of the original WPT features, we then applied Principle Component Analysis (PCA) for feature space dimensionality reduction. Based on our experimental results, WPT features demonstrated high information content and provided a significant statistical difference between normal vs. distracted driving scenarios. Show less