Emotional dysregulation is a common challenge in children with autism spectrum disorder (ASD), often present as sudden tantrums or mood swings. Continuous monitoring of physiological signals offers a nonintrusive way to understand and potentially anticipate these episodes. This paper presents a work-in-progress case study that provides a longitudinal analysis of three months of multimodal physiological data collected from a 10-year-old child diagnosed with ASD using the Empatica EmbracePlus wearable device. Key signals were analyzed, including electrodermal activity (EDA), heart rate (HR), and accelerometry, to identify patterns linked to emotional dysregulation. To validate events, the caregiver installed video cameras throughout the home and reviewed footage whenever the wearable system flagged abnormal physiological activity. This retrospective confirmation enabled accurate annotation of heightened arousal episodes and supported the use of the system as a real-time assistive tool for a working parent to monitor their child's emotional state. Temporal and arousal-based features were extracted using rolling-window and peak-detection methods. Unsupervised anomaly detection Algorithms were employed to differentiate heightened arousal states from baseline behavior. Results revealed consistent physiological signatures, particularly in EDA and HR, that preceded periods of elevated arousal by several minutes.
Micromobility has brought significant changes to the transportation landscape, disrupting traditional transportation methods that have been in place for nearly a century. Abu Dhabi is experiencing an increasing adoption of micromobility, but it also raises concerns about safety hazards for the public. To address these challenges and promote accessibility and safety, it is crucial to establish comprehensive legislation and regulations for micromobility. To develop effective micromobility policies and infrastructure in Abu Dhabi, it is essential to have comprehensive data on user demographics, public behavior, preferences, usage patterns, and trends. Given the city's diverse population and advanced transport system, this data will provide valuable insights and perspectives. Therefore, the objective of this study is to identify the demographic characteristics of individuals who utilize shared e-scooters in Abu Dhabi. The proposed methodology involves the use of Geoinformatics to create a geodatabase (GIS) by collecting relevant data and visually representing potential households and users of e-scooters. The findings of the study indicate that shared e-scooters in Abu Dhabi are predominantly used for leisure purposes, as evidenced by the frequent trips to and from parks. Additionally, the study assesses the usage of escooters as a "first-mile" and "last-mile" transportation option in conjunction with public transit. It reveals that approximately 30% of individuals utilize e-scooters to connect with public bus transportation. Overall, this study aims to provide valuable insights into the demographics and usage patterns of public shared e-scooters in Abu Dhabi, contributing to the development of effective micromobility policies and infrastructure in the city.
Worker productivity is a major concern for the construction industry. Many studies assessed the effect of various factors, such as the work environment and worker health, on productivity. Nevertheless, the extent to which an automatic productive assessment can benefit from wearable electronic-based sensor technologies for physiological and psychological tracking purposes has not yet been fully investigated. This work assesses the ability of capturing the effect of construction workers' happiness on their productivity using physiological signals collected via wearable sensors. Data from both a traditional tracking process (human annotators) and an automated worker physiological signal tracking process that was designed for the purposes of this study were compiled. By considering the traditional tracking process as the baseline for the comparison, this study evaluated the effectiveness of automating happiness tracking as a leading indicator of construction workers' productivity. The physiological signal data collected included blood volume pulse (BVP), respiration rate (RR), heart rate (HR), galvanic skin response (GSR), and skin temperature (TEMP). These data were obtained from a 4-day field study conducted at a pre-fabricated stone construction factory. The study concluded that a moderate positive correlation exists between a worker's emotional status and his productivity exists, with a p-value = 5.5 × 10–8 and a Pearson's coefficient of 0.43.
Recently, deep-learning (DL)-based crack-detection systems have proven to be the method of choice for image processing-based inspection systems. However, human-like generalization remains challenging, owing to a wide variety of factors such as crack type and size. Additionally, because of their localized receptive fields, CNNs have a high false-detection rate and perform poorly when attempting to capture the relevant areas of an image. This study aims to propose a vision-transformer-based crack-detection framework that treats image data as a succession of small patches, to retrieve global contextual information (GCI) through self-attention (SA) methods, and which addresses the CNNs’ problem of inductive biases, including the locally constrained receptive-fields and translation-invariance. The vision-transformer (ViT) classifier was tested to enhance crack classification, localization, and segmentation performance by blending with a sliding-window and tubularity-flow-field (TuFF) algorithm. Firstly, the ViT framework was trained on a custom dataset consisting of 45K images with 224 × 224 pixels resolution, and achieved accuracy, precision, recall, and F1 scores of 0.960, 0.971, 0.950, and 0.960, respectively. Secondly, the trained ViT was integrated with the sliding-window (SW) approach, to obtain a crack-localization map from large images. The SW-based ViT classifier was then merged with the TuFF algorithm, to acquire efficient crack-mapping by suppressing the unwanted regions in the last step. The robustness and adaptability of the proposed integrated-architecture were tested on new data acquired under different conditions and which were not utilized during the training and validation of the model. The proposed ViT-architecture performance was evaluated and compared with that of various state-of-the-art (SOTA) deep-learning approaches. The experimental results show that ViT equipped with a sliding-window and the TuFF algorithm can enhance real-world crack classification, localization, and segmentation performance.
The use of the powered micro-mobility devices has increased significantly around the world, especially with the development of the new electric transportation system. In this research, a questionnaire survey was conducted to provide policymakers with guidelines to encourage users to use micro-vehicles. The survey explores the usage patterns of micro-mobility vehicles in the Emirate of Abu Dhabi, focusing on the types of infrastructure used and the frequency of usage. The research aims to determine what factors would encourage users to use their devices more frequently, with a focus on safety and convenience. According to the survey results, the vast majority of micro-mobility users in Abu Dhabi are currently riding on off-road paths shared with pedestrians, with only slight variations among different micro-mobility types. It was also found that the most common type of infrastructure used was off-road shared tracks with pedestrians, followed by off-road sign-posted tracks shared with pedestrians. There has been a variation in the usage frequencies of the different infrastructural facilities for micro-mobility ridership. However, the usage frequency of micro-vehicles on-road and off-road signposted lanes is almost the same across all the survey locations in Abu Dhabi. The findings of this paper can be used by policymakers to improve micro-mobility and make them more accessible to people.
No abstract is provided for this article.
Existing lifecycle assessment models narrowly concentrate on limited parameters, disregarding system ecology and multi-stage stakeholder integration with sequestered analyses. Assimilation of new transport technologies with traditional design-bid-build decision-making paradigm should acknowledge sustainability triple-bottom-line social, cost, and environmental impacts, across all lifecycle stages for a system-wide optimisation. This study presents a multilevel framework to evaluate road transport system projects utilising 1) stakeholder engagement for flexibility in establishing key performance indicators, 2) passenger surveys to identify what encourages mode choice towards public transport, 3) microsimulation models, lifecycle cost and lifecycle analysis for quantitative assessment, 4) analytic hierarchy for qualitative assessment, and finally 5) multi-criteria modelling with stakeholder expert group preferences. Framework is applied to a 5-lane dual carriageway project in Abu Dhabi, analysing eight virgin and recycled material-based and public transport service alternatives. Study revealed the significance of saved user time, reduced fuel consumption during use phase and vehicle operational costs as the highest lifecycle impact generators. The total sustainability performance across the lifecycle for all alternatives was affected by traffic gridlock formation due to traffic growth in later years and lack of traffic load reduction strategies. Results showed that an autonomous vehicle-based bus rapid transit service coupled with recycled material to construct the road significantly reduced lifecycle cost (51.1%), energy (55.6%) and pollutant (54.5%-CO2, 49.7%-NOx, 24.1%-PM) burdens. It exhibited effectiveness of system choice using lifecycle assessment over the typically fragmented project planning and evaluation approach. Overall, framework exhibited robustness against unit cost and LCI uncertainties, fuel distribution assumptions, discount rate, and indicator weights variations based on priorities of different stakeholder groups.
Crack Detection and Localization in Stone Floor Tiles using Vision Transformer approach Luqman Ali , Hamad Aljassmi , Medha Mohan Ambali Parambil , Muhammed Swavaf , Mohammed AlAmeri , Fady Alnajjar Pages 699-705 (2023 Proceedings of the 40th ISARC, Chennai, India, ISBN 978-0-6458322-0-4, ISSN 2413-5844) Abstract: Cracks are the initial indicators of deterioration of any civil infrastructures. Structures are typically monitored manually by inspectors, which is time consuming, laborious, costly, and easily prone to human error. To address these limitations this paper aims to present a vision transformer-based stone floor tiles crack detection and localization approach. The proposed model is trained on a custom dataset acquired from various stone tiles under various illumination condition in United Arab Emirates. The dataset consists of 5800 images having resolution of 224×224 pixels. The evaluation metrics, testing accuracy, precision, recall, F1 score and computational are used to analyze the performance of the proposed model. The input patch size of the ViT model is varied to investigate its effect on the performance of the model. The experimental results shows that input patch size has significant on the performance of the models. The ViT model trained on the lowest patch size of 14×14 pixel achieved the highest the testing accuracy, precision, recall and F1 score of 0.8612, 0.8840, 0.8304 and 0.8564 respectively. The inference time of the ViT model for a single patch is 0.092 sec. The crack localization is performed by combining the proposed trained ViT model with the sliding window approach. Overall, the proposed model showed promising performance and can used in detection and localization of cracks in stone floor tiles structures. Keywords: Structural Health Monitoring, Crack Detection, Vision Transformer, Sliding Window Approach, Stone floor, automatic inspection. DOI: https://doi.org/10.22260/ISARC2023/0097 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley Presentation Video: https://youtu.be/mXEsAEI1x1M
U-net, a fully convolutional network-based image segmentation method, has demonstrated widespread adaptability in the crack segmentation task. The combination of the semantically dissimilar features of the encoder (shallow layers) and the decoder (deep layers) in the skip connections leads to blurry features map and leads to undesirable over- or under-segmentation of target regions. Additionally, the shallow architecture of the U-Net model prevents the extraction of more discriminatory information from input images. This paper proposes a Residual Sharp U-Net (RS-Net) architecture for crack segmentation and severity assessment in pavement surfaces to address these limitations. The proposed architecture uses residual block in the U-Net model to extract a more insightful representation of features. In addition to that, a sharpening kernel filter is used instead of plain skip connections to generate a fine-tuned encoder features map before combining it with decoder features maps to reduce the dissimilarity between them and smoothes artifacts in the network layers during early training. The proposed architecture is also integrated with various morphological operations to assess the severity of cracks and categorize them into hairline, medium, and severe labels. Experiments results demonstrated that the RS-Net model has promising segmentation performance, outperforming earlier U-Net variations on testing data for crack segmentation and severity assessment, with a promising accuracy (>0.97)
The world has faced the COVID-19 pandemic for over two years now, and it is time to revisit the lessons learned from lockdown measures for theoretical and practical epidemiological improvements. The interlink between these measures and the resulting change in mobility (a predictor of the disease transmission contact rate) is uncertain. We thus propose a new method for assessing the efficacy of various non-pharmaceutical interventions (NPI) and examine the aptness of incorporating mobility data for epidemiological modelling. Facebook mobility maps for the United Arab Emirates are used as input datasets from the first infection in the country to mid-Oct 2020. Dataset was limited to the pre-vaccination period as this paper focuses on assessing the different NPIs at an early epidemic stage when no vaccines are available and NPIs are the only way to reduce the reproduction number ( R0 ). We developed a travel network density parameter βt to provide an estimate of NPI impact on mobility patterns. Given the infection-fatality ratio and time lag (onset-to-death), a Bayesian probabilistic model is adapted to calculate the change in epidemic development with βt . Results showed that the change in βt clearly impacted R0 . The three lockdowns strongly affected the growth of transmission rate and collectively reduced R0 by 78% before the restrictions were eased. The model forecasted daily infections and deaths by 2% and 3% fractional errors. It also projected what-if scenarios for different implementation protocols of each NPI. The developed model can be applied to identify the most efficient NPIs for confronting new COVID-19 waves and the spread of variants, as well as for future pandemics.
Growing demand for road infrastructures and accompanying environmental footprint calls for the replacement of pavement materials with recycled options. The complexities in real-world usability are dependent upon project-specific characteristics and are affected by budgetary constraints of local governmental agencies, material applicability, and climatical conditions. This study conducts a comprehensive lifecycle cost analysis (LCCA) of an urban highway section “E10” in the hot Middle Eastern climate of Abu Dhabi, where virgin asphalt usage is dominant, using actual cost data under multiple scenarios and recycled construction waste (RCW) usage across aggregate layers and recycled asphalt pavement (RAP) across wearing, binder, and asphalt base courses. Blast furnace slag as partial cement replacement for road concrete works is also analysed. Impacts across all lifecycle stages from initial earthworks and construction to routine maintenance and operation were compared. Results found that cost of sustainable construction is lower. Cost reduction was highest for RAP and RCW usage, particularly when the usage was accumulated. The optimum cost scenario used 25% RCW in the sub-base, 80% RCW in the unbound base, 25% warm-mix asphalt (WMA) RAP in the asphalt base, 15% warm-mix RAP in the binder and wearing courses, and 65% slag for concrete roadworks and resulted in USD 2.6 million (15%) cost reduction over 30 years from 2015 to 2045.
This study aims to create and examine a multimodal dataset to enhance crowd management during the Hajj seasons. Sixty-four participants were engaged in Hajj rituals such as Tawaf, Saai, prayer, and Doaa providing location and peripheral physiological data, collected and annotated using a custom-made smartphone application. The collected data was leveraged to conduct a comprehensive analysis, specifically focusing on the classification of the type of Hajj activity, level of fatigue, and emotional states based on peripheral physiological signals. Three deep learning classification models were developed and validated using feedforward neural networks. The models achieved satisfactory accuracy scores in classifying the type of Hajj activity (41.71%), level of fatigue (85.27%), and emotional states (82.47%). While presenting a straightforward use case, this research chiefly provides decision makers and the scientific community with a statistically significant open data set aside with a deep learning architecture capable of characterizing crowd behavior for the purpose of automating crowd management and monitoring.
The first step toward developing an automated construction workers performance monitoring system is to initially establish a complete and competent activity recognition solution, which is still lacking. This study aims to propose a novel approach of using labor physiological data collected through wearable sensors as means of remote and automatic activity recognition.,A pilot study is conducted against three pre-fabrication stone construction workers throughout three full working shifts to test the ability of automatically recognizing the type of activities they perform in-site through their lively measured physiological signals (i.e. blood volume pulse, respiration rate, heart rate, galvanic skin response and skin temperature). The physiological data are broadcasted from wearable sensors to a tablet application developed for this particular purpose, and are therefore used to train and assess the performance of various machine-learning classifiers.,A promising result of up to 88% accuracy level for activity recognition was achieved by using an artificial neural network classifier. Nonetheless, special care needs to be taken for some activities that evoke similar physiological patterns. It is expected that blending this method with other currently developed camera-based or kinetic-based methods would yield higher activity recognition accuracy levels.,The proposed method complements previously proposed labor tracking methods that focused on monitoring labor trajectories and postures, by using additional rich source of information from labors physiology, for real-time and remote activity recognition. Ultimately, this paves for an automated and comprehensive solution with which construction managers could monitor, control and collect rich real-time data about workers performance remotely.
In this study a novel microsimulation-based methodology for environmental assessment of urban systems is developed to address the performance of autonomous mass-mobility against conventional approaches. Traffic growth and microsimulation models, calibrated using real data, are utilised to assess four traffic management scenarios: business-as-usual; public bus transport case; public-bus rapid transit (BRT) case; and, a traffic-demand-responsive-autonomous-BRT case, focusing on fuel energy efficiency, headways, fleet control and platooning for lifecycle analysis (2015–2045) of a case study 3.5 km long 5-lane dual-carriageway section. Results showed that both energy consumption and exhaust emission rates depend upon traffic volume and flow rate factors of vehicle speed-time curves; acceleration-deceleration; and braking rate. The results measured over-reliance of private cars utilising fossil fuel that cause congestions and high environmental footprint on urban roads worsen causing excessive travel times. Public transport promotion was found to be an effective and easy-to-implement environmental burden reduction strategy. Results showed significant potential of autonomous mass-mobility systems to reduce environmental footprint of urban traffic, provided adequate mode-shift can be achieved. The study showed utility of microsimulations for energy and emissions assessment, it linked bus network performance assessment with environmental policies and provided empirical models for headway and service frequency comparisons at vehicle levels. The developed traffic fleet operation prediction methodology for long-term policy implications and tracking models for accurate yearly simulation of real-world vehicle operation profiles are applicable for other sustainability-oriented urban traffic management studies.
