201 research outputs found
Understanding Psycholinguistic Behavior of predominant drunk texters in Social Media
In the last decade, social media has evolved as one of the leading platform
to create, share, or exchange information; it is commonly used as a way for
individuals to maintain social connections. In this online digital world,
people use to post texts or pictures to express their views socially and create
user-user engagement through discussions and conversations. Thus, social media
has established itself to bear signals relating to human behavior. One can
easily design user characteristic network by scraping through someone's social
media profiles. In this paper, we investigate the potential of social media in
characterizing and understanding predominant drunk texters from the perspective
of their social, psychological and linguistic behavior as evident from the
content generated by them. Our research aims to analyze the behavior of drunk
texters on social media and to contrast this with non-drunk texters. We use
Twitter social media to obtain the set of drunk texters and non-drunk texters
and show that we can classify users into these two respective sets using
various psycholinguistic features with an overall average accuracy of 96.78%
with very high precision and recall. Note that such an automatic classification
can have far-reaching impact - (i) on health research related to addiction
prevention and control, and (ii) in eliminating abusive and vulgar contents
from Twitter, borne by the tweets of drunk texters.Comment: 6 pages, 8 Figures, ISCC 2018 Workshops - ICTS4eHealth 201
Understanding psycholinguistic behavior of predominant drunk texters in social media
In the last decade, social media has evolved as one of the leading platform to create, share, or exchange information; it is commonly used as a way for individuals to maintain social connections. In this online digital world, people use to post texts or pictures to express their views socially and create user-user engagement through discussions and conversa
A Machine Learning Approach to Predicting Alcohol Consumption in Adolescents From Historical Text Messaging Data
Techniques based on artificial neural networks represent the current state-of-the-art in machine learning due to the availability of improved hardware and large data sets. Here we employ doc2vec, an unsupervised neural network, to capture the semantic content of text messages sent by adolescents during high school, and encode this semantic content as numeric vectors. These vectors effectively condense the text message data into highly leverageable inputs to a logistic regression classifier in a matter of hours, as compared to the tedious and often quite lengthy task of manually coding data. Using our machine learning approach, we are able to train a logistic regression model to predict adolescents\u27 engagement in substance abuse during distinct life phases with accuracy ranging from 76.5% to 88.1%. We show the effects of grade level and text message aggregation strategy on the efficacy of document embedding generation with doc2vec. Additional examination of the vectorizations for specific terms extracted from the text message data adds quantitative depth to this analysis. We demonstrate the ability of the method used herein to overcome traditional natural language processing concerns related to unconventional orthography. These results suggest that the approach described in this thesis is a competitive and efficient alternative to existing methodologies for predicting substance abuse behaviors. This work reveals the potential for the application of machine learning-based manipulation of text messaging data to development of automatic intervention strategies against substance abuse and other adolescent challenges
Analysis and development of a novel algorithm for the in-vehicle hand-usage of a smartphone
Smartphone usage while driving is unanimously considered to be a really
dangerous habit due to strong correlation with road accidents. In this paper,
the problem of detecting whether the driver is using the phone during a trip is
addressed. To do this, high-frequency data from the triaxial inertial
measurement unit (IMU) integrated in almost all modern phone is processed
without relying on external inputs so as to provide a self-contained approach.
By resorting to a frequency-domain analysis, it is possible to extract from the
raw signals the useful information needed to detect when the driver is using
the phone, without being affected by the effects that vehicle motion has on the
same signals. The selected features are used to train a Support Vector Machine
(SVM) algorithm. The performance of the proposed approach are analyzed and
tested on experimental data collected during mixed naturalistic driving
scenarios, proving the effectiveness of the proposed approach
Automatic driver distraction detection using deep convolutional neural networks
Recently, the number of road accidents has been increased worldwide due to the distraction of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of the people. Therefore, it is essential to monitor and analyze the driver's behavior during the driving time to detect the distraction and mitigate the number of road accident. To detect various kinds of behavior like- using cell phone, talking to others, eating, sleeping or lack of concentration during driving; machine learning/deep learning can play significant role. However, this process may need high computational capacity to train the model by huge number of training dataset. In this paper, we made an effort to develop CNN based method to detect distracted driver and identify the cause of distractions like talking, sleeping or eating by means of face and hand localization. Four architectures namely CNN, VGG-16, ResNet50 and MobileNetV2 have been adopted for transfer learning. To verify the effectiveness, the proposed model is trained with thousands of images from a publicly available dataset containing ten different postures or conditions of a distracted driver and analyzed the results using various performance metrics. The performance results showed that the pre-trained MobileNetV2 model has the best classification efficiency. © 2022 The Author(s
Human-Centric Detection and Mitigation Approach for Various Levels of Cell Phone-Based Driver Distractions
abstract: Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.Dissertation/ThesisDoctoral Dissertation Applied Psychology 201
AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities
Accident detection and traffic analysis is a critical component of smart city
and autonomous transportation systems that can reduce accident frequency,
severity and improve overall traffic management. This paper presents a
comprehensive analysis of traffic accidents in different regions across the
United States using data from the National Highway Traffic Safety
Administration (NHTSA) Crash Report Sampling System (CRSS). To address the
challenges of accident detection and traffic analysis, this paper proposes a
framework that uses traffic surveillance cameras and action recognition systems
to detect and respond to traffic accidents spontaneously. Integrating the
proposed framework with emergency services will harness the power of traffic
cameras and machine learning algorithms to create an efficient solution for
responding to traffic accidents and reducing human errors. Advanced
intelligence technologies, such as the proposed accident detection systems in
smart cities, will improve traffic management and traffic accident severity.
Overall, this study provides valuable insights into traffic accidents in the US
and presents a practical solution to enhance the safety and efficiency of
transportation systems.Comment: 8,
Identification of road user related risk factors, deliverable 4.1 of the H2020 project SafetyCube.
Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the
most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the first deliverable (4.1) of work package 4 which is dedicated to identifying and assessing human related risk factors and corresponding countermeasures as well as their effect on
road safety. The focus of deliverable 4.1 is on identification and assessment of risk factors and describes the corresponding operational procedure and corresponding outcomes. The following steps have been carried out:
Identification of human related risk factors – creation of a taxonomy
Consultation of relevant stakeholders and policy papers for identification of topic with high priority (‘hot topics’)
Systematic literature search and selection of relevant studies on identified risk factors
•Coding of studies
•Analysis of risk factors on basis of coded studies
•Synopses of risk factors, including accident scenarios The core output of this task are synopses of risk factors which will be available through the DSS. Within the synopses, each risk factor was analysed systematically on basis of scientific studies and is further assigned to one of four levels of risk (marked with a colour code). Essential information of the more than 180 included studies were coded and will also be available in the database of the DSS. Furthermore, the synopses contain theoretical background on the risk factor and are prepared in different sections with different levels of detail for an academic as well as a non-academic audience. These sections are readable independently. It is important to note that the relationship between road safety and road user related risk factors is a difficult task. For some risk factors the available studies focused more on conditions of the behaviour (in which situations the behaviour is shown or which groups are more likely to show this
behaviour) rather than the risk factor itself. Therefore, it cannot be concluded that those risk factors that have not often been studied or have to rely more indirect and arguably weaker methodologies, e.g. self-reports , do not increase the chance of a crash occurring. The following analysed risk factors were assessed as ‘risky’, ‘probably risky’ or ‘unclear’. No risk
factors were identified as ‘probably not risky’.
Risky Probably risky Unclear
• Influenced driving – alcohol
• Influenced Driving – drugs
(legal & illegal)
• Speeding and inappropriate
speed
• Traffic rule violations – red
light running
• Distraction – cell phone use
(hand held)
• Distraction – cell phone use
(hands free)
• Distraction – cell phone use
(texting)
• Fatigue – sleep disorders –
sleep apnea
• Risk taking – overtaking
• Risk taking – close following
behaviour
• Insufficient knowledge and
skills
• Functional impairment –
cognitive impairment
• Functional impairment –
vision loss
• Diseases and disorders –
diabetes
• Personal factors – sensation
seeking
• Personal factors – ADHD
• Emotions – anger, aggression
• Fatigue – Not enough
sleep/driving while tired
• Distraction – conversation
with passengers
• Distraction – outside of
vehicle
• Distraction – cognitive
overload and inattention
• Functional impairment –
hearing loss (few studies)
• Observation errors (few studies)
• Distraction – music –
entertainment systems (many
studies, mixed results)
• Distraction – operating devices
(many studies, mixed results)
The next step in SafetyCube’s WP4 is to identify and assess the effectiveness of measures and to establish a link to the identified risk factors. The work of this first task indicates a set of risk factors
that should be centre of attention when identifying corresponding road safety measures (category ‘risky’)
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