Driving Ability Evaluation and Rehabilitation for People With Alzheimer's Disease and Related Dementias.

Worldwide, it is estimated that around 50 million older adults have Alzheimer's disease and related dementias (ADRD). Cognitive deficits associated with ADRD may affect a driver's perception and decision-making and potentially cause safety concerns. Despite much research, there lacks a comprehensive cognitive evaluation to determine the driving capability of a person with ADRD and it is unclear what are the most effective training and interventions that help to enhance driving performance for these individuals. The purpose of this article is to conduct a comprehensive literature survey to review and summarize studies of driving performance evaluation and intervention for people with ADRD and discuss perspectives for future studies. Although many studies have investigated the correlations between driving behaviors and cognitive performances for people with ADRD, it remains unclear how driving behaviors and cognitive performances are associated with psychophysiological measures. We discussed the need to develop regular driving evaluation and rehabilitation protocol for people with ADRD. We also highlighted the potential benefit to combine driving tests with psychophysiological measures to assist in characterizing personalized cognitive evaluation in the behavioral evaluation process.

An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities.

Nowadays, there is a growing trend in smart cities. Therefore, Terrestrial and Internet of Things (IoT) enabled Underwater Wireless Sensor Networks (TWSNs and IoT-UWSNs) are mostly used for observing and communicating via smart technologies. For the sake of collecting the desired information from the underwater environment, multiple acoustic sensors are deployed with limited resources, such as memory, battery, processing power, transmission range, etc. The replacement of resources for a particular node is not feasible due to the harsh underwater environment. Thus, the resources held by the node needs to be used efficiently to improve the lifetime of a network. In this paper, to support smart city vision, a terrestrial based "Away Cluster Head with Adaptive Clustering Habit" (ACH) 2 is examined in the specified three dimensional (3-D) region inside the water. Three different cases are considered, which are: single sink at the water surface, multiple sinks at water surface,, and sinks at both water surface and inside water. "Underwater (ACH) 2 " (U-(ACH) 2 ) is evaluated in each case. We have used depth in our proposed U-(ACH) 2 to examine the performance of (ACH) 2 in the ocean environment. Moreover, a comparative analysis is performed with state of the art routing protocols, including: Depth-based Routing (DBR) and Energy Efficient Depth-based Routing (EEDBR) protocol. Among all of the scenarios followed by case 1 and case 3, the number of packets sent and received at sink node are maximum using DEEC-(ACH) 2 protocol. The packets drop ratio using TEEN-(ACH) 2 protocol is less when compared to other algorithms in all scenarios. Whereas, for dead nodes DEEC-(ACH) 2 , LEACH-(ACH) 2 , and SEP-(ACH) 2 protocols' performance is different for every considered scenario. The simulation results shows that the proposed protocols outperform the existing ones.

Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone.

Sedentary behaviour is increasing due to societal changes and is related to prolonged periods of sitting. There is sufficient evidence proving that sedentary behaviour has a negative impact on people's health and wellness. This paper presents our research findings on how to mine the temporal contexts of sedentary behaviour by utilizing the on-board sensors of a smartphone. We use the accelerometer sensor of the smartphone to recognize user situations (i.e., still or active). If our model confirms that the user context is still, then there is a high probability of being sedentary. Then, we process the environmental sound to recognize the micro-context, such as working on a computer or watching television during leisure time. Our goal is to reduce sedentary behaviour by suggesting preventive interventions to take short breaks during prolonged sitting to be more active. We achieve this goal by providing the visualization to the user, who wants to monitor his/her sedentary behaviour to reduce unhealthy routines for self-management purposes. The main contribution of this paper is two-fold: (i) an initial implementation of the proposed framework supporting real-time context identification; (ii) testing and evaluation of the framework, which suggest that our application is capable of substantially reducing sedentary behaviour and assisting users to be active.

mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification.

The emerging research on automatic identification of user's contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user's contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.

Towards A Self Adaptive System for Social Wellness.

Advancements in science and technology have highlighted the importance of robust healthcare services, lifestyle services and personalized recommendations. For this purpose patient daily life activity recognition, profile information, and patient personal experience are required. In this research work we focus on the improvement in general health and life status of the elderly through the use of an innovative services to align dietary intake with daily life and health activity information. Dynamic provisioning of personalized healthcare and life-care services are based on the patient daily life activities recognized using smart phone. To achieve this, an ontology-based approach is proposed, where all the daily life activities and patient profile information are modeled in ontology. Then the semantic context is exploited with an inference mechanism that enables fine-grained situation analysis for personalized service recommendations. A generic system architecture is proposed that facilitates context information storage and exchange, profile information, and the newly recognized activities. The system exploits the patient's situation using semantic inference and provides recommendations for appropriate nutrition and activity related services. The proposed system is extensively evaluated for the claims and for its dynamic nature. The experimental results are very encouraging and have shown better accuracy than the existing system. The proposed system has also performed better in terms of the system support for a dynamic knowledge-base and the personalized recommendations.

Context representation and fusion: advancements and opportunities.

The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems' design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems.

An adaptive semantic based mediation system for data interoperability among Health Information Systems.

Heterogeneity in the management of the complex medical data, obstructs the attainment of data level interoperability among Health Information Systems (HIS). This diversity is dependent on the compliance of HISs with different healthcare standards. Its solution demands a mediation system for the accurate interpretation of data in different heterogeneous formats for achieving data interoperability. We propose an adaptive AdapteR Interoperability ENgine mediation system called ARIEN, that arbitrates between HISs compliant to different healthcare standards for accurate and seamless information exchange to achieve data interoperability. ARIEN stores the semantic mapping information between different standards in the Mediation Bridge Ontology (MBO) using ontology matching techniques. These mappings are provided by our System for Parallel Heterogeneity (SPHeRe) matching system and Personalized-Detailed Clinical Model (P-DCM) approach to guarantee accuracy of mappings. The realization of the effectiveness of the mappings stored in the MBO is evaluation of the accuracy in transformation process among different standard formats. We evaluated our proposed system with the transformation process of medical records between Clinical Document Architecture (CDA) and Virtual Medical Record (vMR) standards. The transformation process achieved over 90 % of accuracy level in conversion process between CDA and vMR standards using pattern oriented approach from the MBO. The proposed mediation system improves the overall communication process between HISs. It provides an accurate and seamless medical information exchange to ensure data interoperability and timely healthcare services to patients.

Endogeneity of pedestrian survival time and emergency medical service response time: Variations across disadvantaged and non-disadvantaged communities.

The Vision Zero-Safe Systems Approach prioritizes fast access to Emergency Medical Services (EMS) to improve the survivability of road users in transportation crashes, especially concerning the recent increase in pedestrian-involved crashes. Pedestrian crashes resulting in immediate or early death are considerably more severe than those taking longer. The time gap between injury and fatality is known as survival time, and it heavily relies on EMS response time. The characteristics of the crash location may be associated with EMS response and survival time. A US Department of Transportation initiative identifies communities often facing challenges. Six disadvantaged community (DAC) indicators, including economy, environment, equity, health, resilience, and transportation access, enable an analysis of how survival and EMS response times vary across DACs and non-DACs. To this end, this study created a unique and comprehensive database by linking DACs data with 2017-2021 pedestrian-involved fatal crashes. This study utilizes two-stage residual inclusion models with segmentation for DACs and non-DACs accounting for the endogenous relationship between EMS response and pedestrian survival time. The results indicate that EMS response time is higher and pedestrian survival time is lower in DACs than in non-DACs. A delayed EMS response time is associated with a greater reduction in survival time in DACs compared to non-DACs. Factors, e.g., nighttime and interstate crashes, contribute to higher EMS response time, while pedestrian drugs, driver speeding, and hit-and-run behaviors are associated with a greater reduction in survival time in DACs than non-DACs. The implications of the findings are discussed in the paper.

Modeling spatiotemporal heterogeneity in interval-censored traffic incident time to normal flow by leveraging crowdsourced data: A geographically and temporally weighted proportional hazard analysis.

Non-recurrent traffic congestion arising from traffic incidents is unpredictable but should be addressed efficiently to mitigate its adverse impacts on safety and travel time reliability. Numerous studies have been conducted about incident clearance time, while the recovery time, due to the limitations of data collection, is often inadvertently neglected in assessing incident-induced duration (i.e., the time from incident occurrence to the normal flow of traffic). Overlooking the recovery time is likely to underestimate the total incident-induced impact. Furthermore, the spatiotemporal heterogeneity of observed factors is not adequately captured in incident duration models. To address these gaps, this study specifically investigated traffic crashes as they reflect safety issues and are the primary cause of non-recurrent congestion. The emerging crowdsourced traffic reports were harnessed to estimate crash recovery time, which can complement the blind zone of fixed detectors. A geographically and temporally weighted proportional hazard (GWTPH) model was developed to untangle factors associated with the interval-censored crash duration. The results show that the GWTPH model outperforms the global model in goodness-of-fit. Many factors present a spatiotemporally heterogeneous effect. For example, the global model merely revealed that deploying dynamic message signs (DMS) shortened the crash time to normal. Notably, the GWTPH model highlights an average reduction of 32.8% with a standard deviation of 31% in time to normal. The study's findings and application of new spatiotemporal techniques are valuable for practitioners to localize strategies for incident management. For instance, deploying DMS can be very helpful in corridors when incidents happen, especially during peak hours.

Safety in higher level automated vehicles: Investigating edge cases in crashes of vehicles equipped with automated driving systems.

With emerging Automated Driving Systems (ADS) representing Automated Vehicles (AVs) of Level 3 or higher as classified by the Society of Automotive Engineers, several AV manufacturers are testing their vehicles on public roadways in the U.S. The safety performance of AVs has become a major concern for the transportation industry. Several ADS-equipped vehicle crashes have been reported to the National Highway Traffic Safety Administration (NHTSA) in recent years. Scrutinizing these crashes can reveal rare or complex scenarios beyond the normal capabilities of AV technologies called "edge cases." Investigating edge-case crashes helps AV companies prepare vehicles to handle these unusual scenarios and, as such, improves traffic safety. Through analyzing the NHTSA data from July 2021 to February 2023, this study utilizes an unsupervised machine learning technique, hierarchical clustering, to identify edge cases in ADS-equipped vehicle crashes. Fifteen out of 189 observations are identified as edge cases, representing 8 % of the population. Injuries occurred in 10 % of all crashes (19 out of 189), but the proportion rose to 27 % for edge cases (4 out of 15 edge cases). Based on the results, edge cases could be initiated by AVs, humans, infrastructure/environment, or their combination. Humans can be identified as one of the contributors to the onset of edge-case crashes in 60 % of the edge cases (9 out of 15 edge cases). The main scenarios for edge cases include unlawful behaviors of crash partners, absence of a safety driver within the AV, precrash disengagement, and complex events challenging for ADS, e.g., unexpected obstacles, unclear road markings, and sudden and unexpected changes in traffic flow, such as abrupt road congestion or sudden stopped traffic from a crash. Identifying and investigating edge cases is crucial for improving transportation safety and building public trust in AVs.