Assistance Systems Research Articles

Assessing advanced driver assistance systems in police vehicles under demanding conditions

Advanced driver-assistance systems (ADAS) are technologies that can enhance drivers’ safety by relieving them from some driving related activities. However, police driving conditions and demands are different from those of civilian drivers. The objective of this study was to assess the impact of ADAS such as forward collision warning (FCW), automatic emergency braking (AEB), and blind spot monitoring (BSM) on police officers’ driving performance, workload, and trust in vehicle safety to provide personalised solutions for police vehicles. A driving simulation study was conducted with 18 police officers. ADAS use was assessed under various driving conditions and while officers were engaged in non-driving related tasks. Findings suggested that the FCW and AEB systems improved officers’ driving performance, while the BSM system had limited effectiveness due to low salience. ADAS were beneficial under normal driving conditions and when officers were using in-vehicle technology; however, they did not help officers in pursuit conditions.

Retraction Note: Simulation of optical imaging detection based on virtual reality assisted technology in intelligent assistance system for martial arts actions

Retraction Note: Simulation of optical imaging detection based on virtual reality assisted technology in intelligent assistance system for martial arts actions

The association of economic difficulties with social and health care costs of children-target trial emulation using complete birth cohort data in Finland.

It is unclear how much costs economic difficulties in families with children incur to the health and social care sector. We examined the health and social service costs after families entered into, and transitioned out of, social assistance used as a proxy measure for economic difficulties. We analyzed register data on all Finnish children born in 1997 and used the non-randomized target trial framework. The two target trials of entry to economic difficulties (social assistance) and continued economic difficulties included 697 680 and 71 131 children-year observations, respectively, in total. Inverse probability treatment weighting techniques were used to make the comparison group similar to the treatment group in terms of health, socioeconomic and demographic-related pretreatment variables. Entry to social assistance use was associated with some 1511-2619€ (50% compared to the control group) higher cumulative health and social care costs of the children three years after their families transitioned to social assistance, compared to the group that did not enter to social assistance system. This difference was primarily attributed to higher social care costs. Continued social assistance use was associated with some 1007-2709€ (31%) higher costs compared to the comparison group that exited social assistance. These findings support an economic argument to prevent families from entering economic difficulties and to help those in such situations to transition out.

Requirement Analysis of Different Variants of a Measurement and Training Station for Older Adults at Risk of Malnutrition and Reduced Mobility: Focus Group Study.

Demographic change is leading to an increasing proportion of older people in the German population and requires new approaches for prevention and rehabilitation to promote the independence and health of older people. Technical assistance systems can offer a promising solution for the early detection of nutritional and physical deficits and the initiation of appropriate interventions. Such a system should combine different components, such as devices for assessing physical and nutritional status, educational elements on these topics, and training and feedback options. The concept is that the whole system can be used independently by older adults (aged ≥70 years) for monitoring and early detection of problems in nutrition or physical function, as well as providing opportunities for intervention. This study aims to develop technical and digital elements for a measurement and training station (MuTs) with an associated app. Through focus group discussions, target group requirements, barriers, and favorable components for such a system were identified. Older adults (aged ≥70 years) were recruited from a community-based setting as well as from a geriatric rehabilitation center. Focus group interviews were conducted between August and November 2022. Following a semistructured interview guideline, attitudes, requirements, preferences, and barriers for the MuTs were discussed. Discussions were stimulated by videos, demonstrations of measuring devices, and participants' ratings of the content presented using rankings. After conducting 1 focus group in the rehabilitation center and 2 in the community, the interview guide was refined, making a more detailed discussion of identified elements and aspects possible. The interviews were recorded, transcribed verbatim, and analyzed using content analysis. A total of 21 older adults (female participants: n=11, 52%; mean age 78.5, SD 4.6 years) participated in 5 focus group discussions. There was a strong interest in the independent measurement of health parameters, such as pulse and hand grip strength, especially among people with health problems who would welcome feedback on their health development. Participants emphasized the importance of personal guidance and interaction before using the device, as well as the need for feedback mechanisms and personalized training for everyday use. Balance and coordination were mentioned as preferred training areas in a MuTs. New training options that motivate and invite people to participate could increase willingness to use the MuTs. The target group is generally open and interested in tracking and optimizing diet and physical activity. A general willingness to use a MuTs independently was identified, as well as a compelling need for guidance and feedback on measurement and training to be part of the station.

Automated robotic systems in surgical practice

The use of robotic systems has long gone beyond experimental medicine. More than 200 thousand operations per year are carried out with the use of just the most popular robotic complex Da Vinci. Further development of robotics will contribute to improvement of quality and accuracy of surgical interventions. Even now, it enables reduction of postoperative complications to almost zero. This review presents the analysis of the results of introducing robots into surgery. An overview of the data presented in PubMed, Cochrane Library, Science Direct and eLIBRARY was performed.

Comprehensive Review of Security Problems in Mobile Robotic Assistant Systems: Issues, Solutions, and Challenges

Nowadays, robots in the modern world are playing an important and increasingly popular role. MRA (Mobile Robotic Assistant) is a type of mobile robot designed to support humans in many different fields, helping to improve efficiency and safety in daily activities, work, or medical treatment. The number of MRAs is increasing and diverse in function, in addition to the ability to collect and process data, MRAs also have the ability to physically interact with users. Therefore, security is one of the important issues to improve the safety and effective operation of MRA. In this paper, through a comprehensive literature review and detailed analysis of the prominent MRA security attacks in recent years (based on criteria such as: attack targets, technologies used, impact level, feasibility, and contribution to addressing overall MRA security issues), a systematic classification by MRA activity fields is conducted. Security attacks, threats, and vulnerabilities are examined from various perspectives, such as hardware attacks or network/system-level attacks, operating systems/application software. Additionally, corresponding security solutions are proposed, compared, and evaluated to enhance MRA security. The paper also addresses challenges and suggests open research directions for the future.

Active Sampling: A Machine-Learning-Assisted Framework for Finite Population Inference with Optimal Subsamples

Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for estimation of finite population characteristics, using active learning and adaptive importance sampling. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements are demonstrated compared to traditional sampling methods.

THE MAIN CHALLENGES OF THE SOCIAL PROTECTION SYSTEM IN POST-PANDEMIC GEORGIA

In the post-pandemic period in Georgia, despite the high rate of economic growth, poverty and social exclusion remain an acute challenge. The social protection mechanisms operating in the country, such as one-time cash assistance, targeted social assistance, and other support services, such as housing and shelter, services for disabled persons, etc., still cannot provide the desired living conditions for citizens. Establishing and further improving a sustainable, fair, and effective social protection system is one of the country's priorities. Ensuring the effectiveness of the targeted social assistance system is a significant task of both the central and municipal governments. The targeted social assistance program operating in Georgia, which assesses the socio-economic status of families according to the updated methodology, tries to realistically evaluate the needs of the socially vulnerable part of the population, children and families with children, and accordingly determine the main directions of social policy. It is also significant to promote the integration of individuals with disabilities, to improve the socio-economic status and living conditions of internally displaced individuals and eco-migrants, and others. The paper analyzes the primary mechanisms, achievements, and challenges of the social protection system in Georgia. Recommendations are presented in the direction of overcoming poverty and raising the population's standard of living.

Bridging the gap: challenges and opportunities of assistive technology loan library systems for individuals with intellectual disabilities in the United States

Purpose In an increasingly technology-driven world, this study explores the accessibility of assistive technology for individuals with intellectual disabilities through assistive technology loan libraries. Assistive technology refers to devices or systems that enhance the ability of individuals with and without disabilities to function and undertake activities of daily living. Materials and methods The study, grounded in the Social Determinants of Health framework, employed multiple methods including both qualitative (interviews) and quantitative (questionnaire) methods, involving 19 participants comprising assistive technology professionals and users. Results Key findings from the study underscore the pivotal role of assistive technology loan libraries, which lend various assistive devices to individuals, enabling them to try these technologies before making a long-term commitment. However, challenges, such as a restricted variety of devices and extended waiting periods for access were highlighted. The research points to a need for improved training for both staff at the loan libraries and the users of the technology. It also suggests that policy enhancements are required to better align with rapid technological advancements and address the uneven regional distribution of these technologies. Conclusions Participants in the study highlighted the importance of expanding the range of assistive devices available in these libraries and improving the efficiency of the service they provide. The study advocates for targeted efforts to enhance the effectiveness and accessibility of assistive technology loan libraries, highlighting their critical role in empowering individuals with intellectual disabilities.

Evaluating the association between fruit and vegetable consumption and chronic disease prevalence among food pantry users in the northeastern United States

Introduction: Fruit and vegetable (FV) consumption can be a protective factor for chronic diseases, but few studies have investigated the association between FV consumption and health risks for chronic disease in the context of the food and nutrition assistance system. The aim of this study was to assess the association between FV consumption and the prevalence of hypertension, type 2 diabetes mellitus, and body mass index (BMI) among food pantry users in small- to mid-sized metropolitan communities in the northeastern United States. Methods: We used data from three health surveys conducted among residents of communities in upstate New York to construct a predictive model of food pantry use. We then applied the model to a regional subset of SMART Behavioral Risk Factor Surveillance System (BRFSS) data collected in the northeastern United States to identify potential food pantry users. We examined the associations between FV intake and diabetes, hypertension, and BMI through univariate and multivariate logistic and linear regressions. Additionally, we investigated food pantry use as a potential modifier of these associations. Results: The analysis dataset included 5,257 respondents, of which 634 individuals (12.06%) were estimated to be food pantry users. Vegetables consumption was associated with decreased odds of hypertension and lower BMI, regardless of food pantry use. Fruits consumption was associated with decreased odds of diabetes regardless of food pantry use. The association between fruit consumption and BMI was modified by the use of food pantry. Among food pantry users, consumption of fruits was associated with a higher BMI, while among food pantry non-users, it was associated with a lower BMI. Conclusion: The overall protective effect of increased FV consumption on chronic disease risks suggest that increasing FV availability in food pantries may not only alleviate hunger but also contribute to better health. Further research is needed to elucidate what is driving the discrepant association between fruit consumption and BMI among food pantry users and non-users.

Research on the Design of Medical Imaging Assisted Diagnosis System Based on Machine Learning

The design and application of medical imaging diagnostic assistance systems have become an indispensable facet of modern medicine. Technologies based on machine learning can handle vast and intricate medical imaging data, offering precise diagnostic support and significantly enhancing the efficiency and accuracy of clinical diagnoses. This study focuses on the development and implementation of an efficient medical imaging diagnostic assistance system utilizing machine learning algorithms such as Principal Component Analysis (PCA), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP). Through the architectural design of intelligent diagnostic assistance systems, data preprocessing, and feature vectorization, combined with the application of classification algorithms, a robust diagnostic support system capable of addressing diverse medical scenarios has been constructed. Performance evaluation experiments indicate that this system demonstrates high accuracy and robustness in processing medical imaging data, holding potential for clinical implementation. The findings of this study provide new insights for the advancement of intelligent diagnostic systems in medical imaging and lay a solid foundation for the development of future clinical diagnostic tools.

Optimization and simulation of a novel multi-energy complementary heat pump system in cold regions

Solar-assisted ground source heat pump systems(SAGSHPSs) and solar- assisted air source heat pump systems (SAASHPSs) are recognized renewable energy technologies for clean and feasible heating and domestic hot water supply. However, in cold regions, the poor thermal comfort of SAASHPSs and the soil thermal imbalance of SAGSHPSs during heating periods must be resolved urgently. Therefore, to provide an efficient and stable heat source, a multi-energy complementary heat pump system(MECHPS) with seasonal heat storage was designed to meet the needs of building energy consumption. The system comprehensively utilizes solar energy and ambient air for seasonal heat storage during non-heating periods, and then directly provides heat coupled with the ground source heat pump during heating periods. The composition and control strategy of the system were introduced. The system was numerically modeled by the simulation software Transient System Simulation Program (TRNSYS), and the main MECHPS modules were experimentally verified. Additionally, to study the optimal capacity of MECHPS in terms of economy and energy savings, the Hooke-Jeeves algorithm was used for MECHPS capacity optimization with the objectives of minimising the annual cost and maximising the annual savings of standard coal. The results showed that compared with traditional SAASHPSs, the MECHPS exhibited better thermal comfort while maintaining the room temperature above 18 °C. Compared to SAGSHPSs, the MECHPS was able to achieve soil thermal balance, and its soil temperature remained largely constant after 15 years of operation. Finally, compared with the initial scheme, the annual cost reduction rate was 4.98 % under the annual cost optimization scheme and an additional 1310 kg of standard coal could be saved under the annual savings of standard coal optimization scheme. The proposed system provides a way to satisfy the building energy supply for widespread application in cold regions.

A Randomized Controlled Trial on Automated Vehicle Technologies for Drivers With Parkinson's Disease.

Parkinson's disease (PD) negatively affects driver fitness. Few studies document the benefits of in-vehicle information systems (IVIS) and advanced-driver assistance systems (ADAS), the focus of this study, for drivers with PD. This study quantified the impact of IVIS and ADAS on the number of on-road driving errors. Drivers with PD (N = 107) drove a vehicle equipped with IVIS and ADAS in traffic. The activation of IVIS and ADAS resulted in fewer driver errors. Specifically, adaptive cruise control reduced the number of speeding errors on the highway. Bradykinesia correlated with driving errors with deactivated systems. Memory impairments correlated with the total number of driving errors with activated systems. Impairments in executive function and visuospatial ability were associated with more errors during system deactivation. IVIS and ADAS reduced the total number of driving errors for PD drivers; ameliorated effects of individual variations; but memory declines posed a challenge while using these technologies.

Recent advances in Machine Learning based Advanced Driver Assistance System applications

In recent years, the rise of traffic in modern cities has demanded novel technology to support the drivers and protect the passengers and other third parties involved in transportation. Thanks to rapid technological progress and innovations, many Advanced Driver Assistance Systems (A/DAS) based on Machine Learning (ML) algorithms have emerged to address the increasing demand for practical A/DAS applications. Fast and accurate execution of A/DAS algorithms is essential for preventing loss of life and property. High-speed hardware accelerators are vital for processing the high volume of data captured by increasingly sophisticated sensors and complex mathematical models’ execution of modern deep learning (DL) algorithms. One of the fundamental challenges in this new era is to design energy-efficient and portable ML-enabled platforms for vehicles to provide driver assistance and safety. This article presents recent progress in ML-driven A/DAS technology to offer new insights for researchers. We covered standard ML models and optimization approaches based on widely accepted open-source frameworks extensively used in A/DAS applications. We have also highlighted related articles on ML and its sub-branches, neural networks (NNs), and DL. We have also reported the implementation issues, bench-marking problems, and potential challenges for future research. Popular embedded hardware platforms such as Field Programmable Gate Arrays (FPGAs), central processing units (CPUs), Graphical Processing Units (GPUs), and Application Specific Integrated Circuits (ASICs) used to implement A/DAS applications are also compared concerning their performance and resource utilization. We have examined the hardware and software development environments used in implementing A/DAS applications and reported their advantages and disadvantages. We provided performance comparisons of usual A/DAS tasks such as traffic sign recognition, road and lane detection, vehicle and pedestrian detection, driver behavior, and multiple tasking. Considering the current research dynamics, A/DAS will remain one of the most popular application fields for vehicular transportation shortly.

Effect of Back Plate Preheating Assistance System and Deep Rolling Process on Microstructure Defects and Axial Force Reduction of Friction Stir Welded AA6061 Joint.

This study investigates the effects of a back plate preheating assistance system and deep rolling (DR) on axial force and tunnel defects during friction stir welding (FSW). Different preheating configurations-advancing side (AS), retreating side (RS), and both sides-were examined to evaluate their impact on axial force reduction, temperature distribution, and defect minimization. Axial force measurements were taken using a dynamometer, and temperature histories were recorded with a thermal camera. The results demonstrate that a preheating temperature of 200 °C is optimal, reducing axial force by 30.24% and enhancing material flow. This temperature also facilitated deeper tool penetration, especially when preheating was applied to both sides. Preheating on the AS resulted in the smallest tunnel defects, reducing defect size by 80.15% on the RS and 96.91% on the AS compared to the non-preheated condition. While DR further reduced tunnel defects, its effectiveness was limited by the proximity of defects to the surface. These findings offer significant insights for improving the FSW process.

Realistic 3D Simulators for Automotive: A Review of Main Applications and Features.

Recent advancements in vehicle technology have stimulated innovation across the automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving and motorsport applications. Modern vehicles, equipped with sensors for perception, localization, navigation, and actuators for autonomous driving, generate vast amounts of data used for training and evaluating autonomous systems. Real-world testing is essential for validation but is complex, expensive, and time-intensive, requiring multiple vehicles and reference systems. To address these challenges, computer graphics-based simulators offer a compelling solution by providing high-fidelity 3D environments to simulate vehicles and road users. These simulators are crucial for developing, validating, and testing ADAS, autonomous driving systems, and cooperative driving systems, and enhancing vehicle performance and driver training in motorsport. This paper reviews computer graphics-based simulators tailored for automotive applications. It begins with an overview of their applications and analyzes their key features. Additionally, this paper compares five open-source (CARLA, AirSim, LGSVL, AWSIM, and DeepDrive) and ten commercial simulators. Our findings indicate that open-source simulators are best for the research community, offering realistic 3D environments, multiple sensor support, APIs, co-simulation, and community support. Conversely, commercial simulators, while less extensible, provide a broader set of features and solutions.

Brain-Controlled Augmented Hearing for Spatially Moving Conversations in Multi-Talker Environments.

Focusing on a specific conversation amidst multiple interfering talkers is challenging, especially for those with hearing loss. Brain-controlled assistive hearing devices aim to alleviate this problem by enhancing the attended speech based on the listener's neural signals using auditory attention decoding (AAD). Departing from conventional AAD studies that relied on oversimplified scenarios with stationary talkers, a realistic AAD task that involves multiple talkers taking turns as they continuously move in space in background noise is presented. Invasive electroencephalography (iEEG) data are collected from three neurosurgical patients as they focused on one of the two moving conversations. An enhanced brain-controlled assistive hearing system that combines AAD and a binaural speaker-independent speech separation model is presented. The separation model unmixes talkers while preserving their spatial location and provides talker trajectories to the neural decoder to improve AAD accuracy. Subjective and objective evaluations show that the proposed system enhances speech intelligibility and facilitates conversation tracking while maintaining spatial cues and voice quality in challenging acoustic environments. This research demonstrates the potential of this approach in real-world scenarios and marks a significant step toward developing assistive hearing technologies that adapt to the intricate dynamics of everyday auditory experiences.

Integrated Thermomechanical Analysis of Tires and Brakes for Vehicle Dynamics and Safety

The accurate prediction of tire and brake thermomechanical behavior is crucial for various applications in the automotive industry, including vehicle dynamics analysis, racing performance optimization, and driver assistance system development. The temperature of the brakes plays a crucial role in determining the performance of rubber by altering its temperature. This change impacts the rim and the air within the tire, leading to variations in temperature and tire pressure, which consequently affect the vehicle’s overall performance. Traditionally, these components have been modeled separately, neglecting the crucial thermal interaction between them, thereby losing a lot of important information from the outside that influences the tire. This paper presents a novel method that overcomes this limitation by coupling the thermomechanical models of the tire and brake, enabling a more comprehensive understanding of their combined behavior. Therefore, the present work could be an interesting starting point to understand how a control system can be influenced by the thermodynamic of the wheel–brake system.

Retraction Note: Design of home fitness assistant system based on android mobile network and optical sensing system

Retraction Note: Design of home fitness assistant system based on android mobile network and optical sensing system

Risk identification and prediction model for continuous‐lane‐change vehicles considering driving style

Lane change risk identification and prediction research is important for traffic safety, development of advanced driver assistance systems, traffic flow optimization. Among them, vehicle continuous‐lane‐change (CLC) risk identification and prediction is weak, but this element is important for vehicle decision-making and path planning in multi-lane, non-adjacent lane situations. To compensate for the shortcomings of risk identification based on both deterministic and probabilistic methods and address the issues of poor measurement accuracy and efficiency caused by manual tuning, this study established a risk identification and prediction model based on the Bayesian global optimization (BO) gated recurrent unit (GRU) neural network (BO-GRU) and instantiated the model using the trajectory data of 129 vehicles. Overall, the results showed that the accuracy of identifying CLC risks and driving-style was 99.33% and 97.44%, respectively, while the overall average accuracy of predicting potential CLC risks based on the identification of risks and predicted trajectories was 97.13%. The root-mean-square errors of CLC predicted trajectory for different driving-styles were 0.2503, 0.2331, 0.2093, and 0.2706, respectively, which were 84%−88% lower compared to 1.7361 when driving-styles were not considered. The performance of the BO-GRU model is comparable to that of parameter-optimized LSTM and its variant models, which highlights the significance of parameter optimization for neural network models. Overall, the use of a BO-GRU-based model for identifying and predicting continuous lane change risks in vehicles can aid in making driving decisions and planning routes, decrease the risk of collisions, enhance traffic flow efficiency, and encourage safe driving behavior.