Biosignals Monitoring for Driver Drowsiness Detection using Deep Neural Networks
Drowsy driving poses a significant risk to road safety, necessitating the development of reliable drowsiness detection systems. In particular, the advancement of Artificial Intelligence based neuroadaptive systems is imperative to effectively mitigate this risk. Towards reaching this goal, the present research focuses on investigating the efficacy of physiological indicators, including heart rate variability (HRV), percentage of eyelid closure over the pupil over time (PERCLOS), blink rate, blink percentage, and electrodermal activity (EDA) signals, in predicting driver drowsiness. The study was conducted with a cohort of 30 participants in controlled simulated driving scenarios, with half driving in a non-monotonous environment and the other half in a monotonous environment. Three deep learning algorithms were employed: sequential neural network (SNN) for HRV, 1D-convolutional neural network (1D-CNN) for EDA, and convolutional recurrent neural network (CRNN) for eye tracking. The HRV-Based Model and EDA-Based Model exhibited strong performance in drowsiness classification, with the HRV model achieving precision, recall, and F1-score of 98.28%, 98%, and 98%, respectively, and the EDA model achieving 96.32%, 96%, and 96% for the same metrics. The confusion matrix further illustrates the model's performance and highlights high accuracy in both HRV and EDA models, affirming their efficiency in detecting driver drowsiness. However, the Eye-Based Model faced difficulties in identifying drowsiness instances, potentially attributable to dataset imbalances and underrepresentation of specific fatigue states. Despite the challenges, this work significantly contributes to ongoing efforts to improve road safety by laying the foundation for effective real-time neuro-adaptive systems for drowsiness detection and mitigation.
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Quantitative Analysis of Physiological and Psychological Impacts of Visual and Auditory Elements in Wuyishan National Park Using Eye-Tracking
Amidst rapid societal changes and increasing urbanization, human connectivity with nature has declined, exacerbating public health concerns. This study assesses the efficacy of Shinrin-yoku, or ‘forest bathing’, in Wuyishan National Park as a simple and effective method to counteract the adverse health effects of contemporary lifestyles. Employing repeated-measures analysis of variance, forty-one participants were observed over three days across eight distinct forest settings. Techniques included eye-tracking for visual attention and soundscape perception assessments via questionnaires. Physiological responses were gauged through heart rate variability and skin conductance, while psychological evaluations utilized the Profile of Mood States (POMS) and Positive and Negative Affect Schedule (PANAS). Findings revealed that (1) natural soundscapes—especially birdsong, flowing water, wind, and bamboo raft sounds—and visual elements, such as distant mountains, streams, trees, Danxia landforms, tea gardens, and bamboo views, play pivotal roles in regulating heart rate variability, reducing arousal, and enhancing stress adaptation. Additionally, cultural landscapes, such as classical music and ancient structures, bolster parasympathetic activity. (2) Natural and cultural auditory stimuli, including flowing water and classical music, coupled with visual features, such as Danxia landforms, streams, distant mountains, lawns, and guide signs, effectively induce positive mood states, regulate mood disturbances, and enhance psychological well-being across diverse forest settings. These findings underscore the significant health benefits of immersive natural experiences and advocate for integrating forest-based wellness programs into public health strategies, offering compelling evidence for enriching life quality through nature engagement.
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Word frequency and cognitive effort in turns-at-talk: turn structure affects processing load in natural conversation
Frequency distributions are known to widely affect psycholinguistic processes. The effects of word frequency in turns-at-talk, the nucleus of social action in conversation, have, by contrast, been largely neglected. This study probes into this gap by applying corpus-linguistic methods on the conversational component of the British National Corpus (BNC) and the Freiburg Multimodal Interaction Corpus (FreMIC). The latter includes continuous pupil size measures of participants of the recorded conversations, allowing for a systematic investigation of patterns in the contained speech and language on the one hand and their relation to concurrent processing costs they may incur in speakers and recipients on the other hand. We test a first hypothesis in this vein, analyzing whether word frequency distributions within turns-at-talk are correlated with interlocutors' processing effort during the production and reception of these turns. Turns are found to generally show a regular distribution pattern of word frequency, with highly frequent words in turn-initial positions, mid-range frequency words in turn-medial positions, and low-frequency words in turn-final positions. Speakers' pupil size is found to tend to increase during the course of a turn at talk, reaching a climax toward the turn end. Notably, the observed decrease in word frequency within turns is inversely correlated with the observed increase in pupil size in speakers, but not in recipients, with steeper decreases in word frequency going along with steeper increases in pupil size in speakers. We discuss the implications of these findings for theories of speech processing, turn structure, and information packaging. Crucially, we propose that the intensification of processing effort in speakers during a turn at talk is owed to an informational climax, which entails a progression from high-frequency, low-information words through intermediate levels to low-frequency, high-information words. At least in English conversation, interlocutors seem to make use of this pattern as one way to achieve efficiency in conversational interaction, creating a regularly recurring distribution of processing load across speaking turns, which aids smooth turn transitions, content prediction, and effective information transfer.
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A comprehensive study of human factors, sensory principles, and commercial solutions for future human-centered working operations in industry 5.0
The purpose of this study is to explore the measurement of human factors in the workplace that can provide critical insights into workers’ well-being. Human factors refer to physical, cognitive, and psychological states that can impact the efficiency, effectiveness, and mental health of workers. The article identifies six human factors that are particularly crucial in today’s workplaces: physical fatigue, attention, mental workload, stress, trust, and emotional state. Each of these factors alters the human physiological response in a unique way, affecting the human brain, cardiovascular, electrodermal, muscular, respiratory, and ocular reactions. This paper provides an overview of these human factors and their specific influence on psycho-physiological responses, along with suitable technologies to measure them in working environments and the currently available commercial solutions to do so. By understanding the importance of these human factors, employers can make informed decisions to create a better work environment that leads to improved worker well-being and productivity.
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Comparative Data Analysis of Older Driver’s vs Younger Driver’s Gap Acceptance Behavior at signalized left turns-A driving Simulator Study
Drivers aged 65 and older are particularly prone to motor vehicle crashes, with approximately 20% of traffic fatalities occurring at intersections [11]. Intersections appear to be hazardous for drivers in this age group due to cognitive, perceptual, and psychomotor challenges. Older drivers find it particularly difficult to safely navigate left turns at signalized permissive intersections, having problems adequately detecting, perceiving, and accurately judging the safety of gaps. The increase in the number of elderly drivers has been paralleled by an increase in road-related accidents due to age-related fragility. By 2030, more than 21% of the adult population is projected to be over 65 years old [1]. However, previous studies have not adequately considered the combined effects of the randomized gap, queue length, traffic volume, pedestrians, and physiological factors on driving. The current study aims to address the gap in the literature by explicitly examining older and younger drivers’ gap acceptance behaviors during permissive left turns at four-way intersections. The main objective of this thesis is to study, identify and analyze the effect of Gap Acceptance Behavior on age, traffic volume, queue length, and physiological factors such as heart rate variability (HRV), electrodermal activity (EDA), and motion sickness among older and younger drivers. The data was collected from a driving simulator study comprising 40 participants aged between 20-30 for younger and 65 years for older. The collected data was used for comparative analysis, with the Gap Accepted by the drivers calculated from the video data. The gap is calculated as the distance between the left turning vehicle and the oncoming traffic. All recruited drivers were healthy. Each participant navigated twelve scenarios, six with lower traffic conditions and six with higher traffic conditions. Each lower and higher traffic scenario varied in queue length, with the number of cars in front of the ego vehicle varying from 0, 1, and 2. All varying queue lengths also had one with a pedestrian and another without. The physiological data collected through the Empatica4 wristband was also considered to study the gap acceptance behavior. Another parameter, motion sickness susceptibility score (MSSQ), was obtained from a questionnaire the participants completed after the experiment. Of these factors, queue length, traffic volume, and pedestrians play a significant role in studying gap acceptance. There is a significant difference in accepting and rejecting the gap between young and older drivers. Older drivers’ decision is affected more by factors, such as traffic volume, age, queue length, HRV, EDA, MSSQ score and the presence of pedestrians. This study showed that older drivers exhibited longer gap acceptance times than their younger counterparts while turning left across traffic at permissive intersections. Researchers may use the findings to better understand gap acceptance behaviors, while policymakers may utilize the results to design mobility guidelines.
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Simulator
Effects of an intelligent virtual assistant on office task performance and workload in a noisy environment
This study examines the effects of noise and the use of an Intelligent Virtual Assistant (IVA) on the task performance and workload of office workers. Data were collected from forty-eight adults across varied office task scenarios (i.e., sending an email, setting up a timer/reminder, and searching for a phone number/address) and noise types (i.e., silence, non-verbal noise, and verbal noise). The baseline for this study is measured without the use of an IVA. Significant differences in performance and workload were found on both objective and subjective measures. In particular, verbal noise emerged as the primary factor affecting performance using an IVA. Task performance was dependent on the task scenario and noise type. Subjective ratings found that participants preferred to use IVA for less complex tasks. Future work can focus more on the effects of tasks, demographics, and learning curves. Furthermore, this work can help guide IVA system designers by highlighting factors affecting performance.
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Experimental research on college students’ emergency ability and its influencing factors in virtual emergency situations
In this paper, the physiological emergency ability of college students and its influencing factors are analyzed using the experimental method. College students are grouped according to gender, age, emergency knowledge mastery, psychological condition, professional background, origin of students, interest in emergency knowledge, whether they have conducted emergency drills, whether they are the only child, etc. The difference between each influencing factor and individual physiological index is significant. It provides a theoretical basis for improving the emergency ability of college students, and makes up for the theoretical gap that few experimental methods are used to study the emergency ability of college students.
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Machine Learning in Driver Drowsiness Detection: A Focus on HRV, EDA, and Eye Tracking
Drowsy driving continues to be a significant cause of road traffic accidents, necessitating the development of robust drowsiness detection systems. This research enhances our understanding of driver drowsiness by analyzing physiological indicators – heart rate variability (HRV), the percentage of eyelid closure over the pupil over time (PERCLOS), blink rate, blink percentage, and electrodermal activity (EDA) signals. Data was collected from 40 participants in a controlled scenario, with half of the group driving in a non-monotonous scenario and the other half in a monotonous scenario. Participant fatigue was assessed twice using the Fatigue Assessment Scale (FAS).The research developed three machine learning models: HRV-Based Model, EDA-Based Model, and Eye-Based Model, achieving accuracy rates of 98.28%, 96.32%, and 90% respectively. These models were trained on the aforementioned physiological data, and their effectiveness was evaluated against a range of advanced machine learning models including GRU, Transformers, Mogrifier LSTM, Momentum LSTM, Difference Target Propagation, and Decoupled Neural Interfaces Using Synthetic Gradients. The HRV-Based Model and EDA-Based Model demonstrated robust performance in classifying driver drowsiness. However, the Eye-Based Model had some difficulty accurately identifying instances of drowsiness, likely due to the imbalanced dataset and underrepresentation of certain fatigue states. The study duration, which was confined to 45 minutes, could have contributed to this imbalance, suggesting that longer data collection periods might yield more balanced datasets. The average fatigue scores obtained from the FAS before and after the experiment showed a relatively consistent level of reported fatigue among participants, highlighting the potential impact of external factors on fatigue levels. By integrating the outcomes of these individual models, each demonstrating strong performance, this research establishes a comprehensive and robust drowsiness detection system. The HRV-Based Model displayed remarkable accuracy, while the EDA-Based Model and the Eye-Based Model contributed valuable insights despite some limitations. The research highlights the necessity of further optimization, including more balanced data collection and investigation of individual and external factors impacting drowsiness. Despite the challenges, this work significantly contributes to the ongoing efforts to improve road safety by laying the foundation for effective real-time drowsiness detection systems and intervention methods.
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Reaching beneath the tip of the iceberg: A guide to the Freiburg Multimodal Interaction Corpus
Most corpora tacitly subscribe to a speech-only view filtering out anything that is not a ‘word’ and transcribing the spoken language merely orthographically despite the fact that the “speech-only view on language is fundamentally incomplete” (Kok 2017, 2) due to the deep intertwining of the verbal, vocal, and kinesic modalities (Levinson and Holler 2014). This article introduces the Freiburg Multimodal Interaction Corpus (FreMIC), a multimodal and interactional corpus of unscripted conversation in English currently under construction. At the time of writing, FreMIC comprises (i) c. 29 h of video-recordings transcribed and annotated in detail and (ii) automatically (and manually) generated multimodal data. All conversations are transcribed in ELAN both orthographically and using Jeffersonian conventions to render verbal content and interactionally relevant details of sequencing (e.g. overlap, latching), temporal aspects (pauses, acceleration/deceleration), phonological aspects (e.g. intensity, pitch, stretching, truncation, voice quality), and laughter. Moreover, the orthographic transcriptions are exhaustively PoS-tagged using the CLAWS web tagger (Garside and Smith 1997 ). ELAN-based transcriptions also provide exhaustive annotations of re-enactments (also referred to as (free) direct speech, constructed dialogue, etc.) as well as silent gestures (meaningful gestures that occur without accompanying speech). The multimodal data are derived from psychophysiological measurements and eye tracking. The psychophysiological measurements include, inter alia, electrodermal activity or GSR, which is indicative of emotional arousal (e.g. Peräkylä et al. 2015 ). Eye tracking produces data of two kinds: gaze direction and pupil size. In FreMIC, gazes are automatically recorded using the area-of-interest technology. Gaze direction is interactionally key, for example, in turn-taking (e.g. Auer 2021) and re-enactments (e.g. Pfeiffer and Weiss 2022 ), while changes in pupil size provide a window onto cognitive intensity (e.g. Barthel and Sauppe 2019). To demonstrate what opportunities FreMIC’s (combination of) transcriptions, annotations, and multimodal data open up for research in Interactional (Corpus) Linguistics, this article reports on interim results derived from work-in-progress.
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