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The appearance of construction defects in buildings can arise from a variety of factors, ranging from issues during the design and construction phases to problems that develop over time with the lifecycle of a building. These defects require repairs, often in the context of a significant shortage of skilled labor. In addition, such work is often physically demanding and carried out in hazardous environments. Consequently, adopting autonomous robotic systems in the construction industry becomes essential, as they can relieve labor shortages, promote safety, and enhance the quality and efficiency of repair and maintenance tasks. Hereupon, the present study introduces an end-to-end framework towards the automation of shotcreting tasks in cases where construction or repair actions are required. The proposed system can scan a construction scene using a stereo-vision camera mounted on a robotic platform, identify regions of defects, and reconstruct a 3D model of these areas. Furthermore, it automatically calculates the required 3D volumes to be constructed to treat a detected defect. To achieve all of the above-mentioned technological tools, the developed software framework employs semantic segmentation and 3D reconstruction modules based on YOLOv8m-seg, SiamMask, InfiniTAM, and RTAB-Map, respectively. In addition, the segmented 3D regions are processed by the volumetric modeling component, which determines the amount of concrete needed to fill the defects. It generates the exact 3D model that can repair the investigated defect. Finally, the precision and effectiveness of the proposed pipeline are evaluated in actual construction site scenarios, featuring reinforcement bars as defective areas. [ABSTRACT FROM AUTHOR]

Due to the accelerated growth of the world's population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure improves the efficacy and accuracy of numerous agricultural duties. In this paper, we present a comprehensive autonomous navigation architecture for holonomic mobile robots in greenhouses. Our approach utilizes the heating system rails to navigate through the crop rows using a single stereo camera for perception and a LiDAR sensor for accurate distance measurements. A finite state machine orchestrates the sequence of required actions, enabling fully automated task execution, while semantic segmentation provides essential cognition to the robot. Our approach has been evaluated in a real-world greenhouse using a custom-made robotic platform, showing its overall efficacy for automated inspection tasks in greenhouses. [ABSTRACT FROM AUTHOR]

During the last few decades, great research endeavors have been applied to healthcare robots, aiming to develop companions that extend the independent living of elderly people. To deploy such robots into the market, it is expected that certain applications should be addressed with repeatability and robustness. Such application is the assistance with medication-related activity, a common need for the majority of elderly people, referred from here on as medication adherence. This paper presents a novel and complete pipeline for assistance provision in monitoring and serving of medication, using a mobile manipulator embedded with action, perception and cognition skills. The challenges tackled in this work comprise, among others, that the robot locates the medication box placed in challenging spots by applying vision based strategies, thus enabling robust grasping. The grasping is performed with strategies that allow environmental contact, accommodated by the manipulator's admittance controller which offers compliance behavior during interaction with the environment. Robot navigation is applied for the medication delivery, which, combined with active vision methods, enables the automatic selection of parking positions, allowing efficient interaction and monitoring of medication intake activity. The robot skills are orchestrated by a partially observable Markov decision process mechanism which is coupled with a task planner. This enables assistance scenario guidance and offers repeatability as well as gentle degradation of the system upon a failure, thus avoiding uncomfortable situations during human–robot interaction. Experiments have been conducted on the full pipeline, including robot's deployment in 12 real house environments with real participants that led to very promising results with valuable findings for similar future applications. [ABSTRACT FROM AUTHOR]

Background: Literature supports the use of serious games and virtual environments to assess cognitive functions and detect cognitive decline. This promising assessment method, however, has not yet been translated into self-administered screening instruments for pre-clinical dementia.Objective: The aim of this study is to assess the performance of a novel self-administered serious game-based test, namely the Virtual Supermarket Test (VST), in detecting mild cognitive impairment (MCI) in a sample of older adults with subjective memory complaints (SMC), in comparison with two well-established screening instruments, the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE).Methods: Two groups, one of healthy older adults with SMC (N = 48) and one of MCI patients (N = 47) were recruited from day centers for cognitive disorders and administered the VST, the MoCA, the MMSE, and an extended pencil and paper neuropsychological test battery.Results: The VST displayed a correct classification rate (CCR) of 81.91% when differentiating between MCI patients and older adults with SMC, while the MoCA displayed of CCR of 72.04% and the MMSE displayed a CCR of 64.89%.Conclusion: The three instruments assessed in this study displayed significantly different performances in differentiating between healthy older adults with SMC and MCI patients. The VST displayed a good CCR, while the MoCA displayed an average CCR and the MMSE displayed a poor CCR. The VST appears to be a robust tool for detecting MCI in a population of older adults with SMC. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate cognitive functioning by administering the Virtual Supermarket (VSM) test in patients with amnestic mild cognitive impairment (aMCI, N = 37) and age and education-matched healthy controls (HCs, N = 52). An extensive neuropsychological test battery and the VSM were administered to all participants. The aMCI group exhibited lower performance and required more time to complete the VSM compared to HCs. Also, aMCI-Multiple Domain (aMCI-MD) patients performed worse in the "Correct Types", "Correct Quantities", "Bought Unlisted", "Correct Money" variables compared to HCs. Moreover, aMCI-SD patients displayed lower performance in "Bought Unlisted" and "Correct Money" variables compared to HCs. The VSM variables correlated with established neuropsychological test scores. The VSM test was found to discriminate between aMCI and HCs with a correct classification rate (CCR) of 81%. This is a preliminary study showing that the VSM is a valid, brief and user-friendly test. . [ABSTRACT FROM AUTHOR]

Background: It has been demonstrated that virtual reality (VR) applications can be used for the detection of mild cognitive impairment (MCI).Objective: The aim of this study is to provide a preliminary investigation on whether a VR cognitive training application can be used to detect MCI in persons using the application at home without the help of an examiner.Methods: Two groups, one of healthy older adults (n = 6) and one of MCI patients (n = 6) were recruited from Thessaloniki day centers for cognitive disorders and provided with a tablet PC with custom software enabling the self-administration of the Virtual Super Market (VSM) cognitive training exercise. The average performance (from 20 administrations of the exercise) of the two groups was compared and was also correlated with performance in established neuropsychological tests.Results: Average performance in terms of duration to complete the given exercise differed significantly between healthy(μ  = 247.41 s/ sd = 89.006) and MCI (μ= 454.52 s/ sd = 177.604) groups, yielding a correct classification rate of 91.8% with a sensitivity and specificity of 94% and 89% respectively for MCI detection. Average performance also correlated significantly with performance in Functional Cognitive Assessment Scale (FUCAS), Test of Everyday Attention (TEA), and Rey Osterrieth Complex Figure test (ROCFT).Discussion: The VR application exhibited very high accuracy in detecting MCI while all participants were able to operate the tablet and application on their own. Diagnostic accuracy was improved compared to a previous study using data from only one administration of the exercise. The results of the present study suggest that remote MCI detection through VR applications can be feasible. [ABSTRACT FROM AUTHOR]

Background: Recent research advocates the potential of virtual reality (VR) applications in assessing cognitive functions highlighting the possibility of using a VR application for mild cognitive impairment (MCI) screening. Objective: The aim of this study is to investigate whether a VR cognitive training application, the virtual supermarket (VSM), can be used as a screening tool for MCI. Methods: Two groups, one of healthy older adults (n = 21) and one of MCI patients (n = 34), were recruited from day centers for cognitive disorders and administered the VSM and a neuropsychological test battery. The performance of the two groups in the VSM was compared and correlated with performance in established neuropsychological tests. At the same time, the effectiveness of a combination of traditional neuropsychological tests and the VSM was examined. Results: VSM displayed a correct classification rate (CCR) of 87.30% when differentiating between MCI patients and healthy older adults, while it was unable to differentiate between MCI subtypes. At the same time, the VSM correlates with various established neuropsychological tests. A limited number of tests were able to improve the CCR of the VSM when combined with the VSM for screening purposes. Discussion: VSM appears to be a valid method of screening for MCI in an older adult population though it cannot be used for MCI subtype assessment. VSM's concurrent validity is supported by the large number of correlations between the VSM and established tests. It is considered a robust test on its own as the inclusion of other tests failed to improve its CCR significantly. [ABSTRACT FROM AUTHOR]

The importance of construction automation has grown worldwide, aiming to deliver new machineries for the automation of roads, tunnels, bridges, buildings and earth-work construction. This need is mainly driven by (i) the shortage and rising costs of skilled workers, (ii) the tremendous increased needs for new infrastructures to serve the daily activities and (iii) the immense demand for maintenance of ageing infrastructure. Shotcrete (sprayed concrete) is increasingly becoming popular technology among contractors and builders, as its application is extremely economical and flexible as the growth in construction repairs in developed countries demand excessive automation of concrete placement. Even if shotcrete technology is heavily mechanized, the actual application is still performed manually at a large extend. RoBétArméEuropean project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. The paper at hand showcases the development of a novel robotic system with advanced perception, cognition and digitization capabilities for the automation of all phases of shotcrete application. In particular, the challenges and barriers in shotcrete automation are presented and the RoBétArmésuggested solutions are outlined. We introduce a basic conceptual architecture of the system to be developed and we demonstrate the four application scenarios on which the system is designated to operate., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Kostavelis I et al.)

This study conducted a preliminary usability assessment of the Virtual Supermarket Test (VST), a serious game-based self-administered cognitive screening test for mild cognitive impairment (MCI). Twenty-four healthy older adults with subjective cognitive decline and 33 patients with MCI self-administered the VST and then completed the System Usability Scale (SUS). The average SUS score was 83.11 (SD = 14.6). The SUS score was unaffected by age, education, touch device familiarity, and diagnosis of MCI. SUS score correlated with VST performance ( r  = -0.496, p  = 0.000). Results of this study indicate good usability of the VST., Competing Interests: The authors have no conflict of interest to report., (© 2022 – The authors. Published by IOS Press.)

Sedentary behavior is considered as a major public health challenge, linked with many chronic diseases and premature mortality. In this paper, we propose a steps counting -based machine learning approach for the prediction of sedentary behavior. Our work focuses on analyzing historical data from multiple users of wearable physical activity trackers and exploring the performance of four machine learning algorithms, i.e., Logistic Regression, Random Forest, XGBoost, Convolutional Neural Networks, as well as a Majority Vote Ensemble of the algorithms. To train and test our models we employed a crowd sourced dataset containing a month's data of 33 users. For further evaluation, we employed a dataset containing 6 months of data of an additional user. The results revealed that while all models succeed in predicting next-day sedentary behavior, the ensemble model outperforms all baselines, as it manages to predict sedentary behavior and reduce false positives more effectively. On the multi-subjects test dataset, our ensemble model achieved an accuracy of 82.12% with a sensitivity of 74.53% and a specificity of 85.71%. On the additional unseen dataset, we achieved 76.88% in accuracy, 63.27% in sensitivity and 81.75% in specificity. These outcomes provide the ground towards the development of real-life artificially intelligent systems for sedentary behavior prediction.

Background: There is a need for new practical tools to assess the cognitive impairment of small vessel disease (SVD) patients in the clinic., Objective: This study aimed to examine cognitive functioning by administering the Virtual Supermarket (VST) in patients with SVD with cognitive impairment (SVD-CI, N = 32), cognitively normal SVD (SVD-CN, N = 37), and age-and education-matched healthy controls (HC, N = 30)., Methods: The tablet-based VST application and comprehensive traditional pencil-and-paper neuropsychological tests assessing memory, attention, executive function, visuospatial function, and language were administered to all participants., Results: A moderate correlation was found between the "Duration" and "Correct Quantities" variables of VST and visuospatial function and general cognitive status composite Z scores across SVD-CI patients. "Duration" and "Correct Money" variables were moderately related to memory, executive functions, and visuospatial function composite Z scores across SVD-CN patients. A combination of all VST variables discriminated SVD-CI and HC with a correct classification rate of 81%, a sensitivity of 78%, and a specificity of 84%., Conclusion: This study is the first to evaluate cognitive functions employing the VST in SVD with and without cognitive impairment. It provides encouraging preliminary findings of the utility of the VST as a screening tool in the assessment of cognitive impairment and the differentiation of SVD patients from HC. In the future, validation studies of the VST with larger samples are needed., Competing Interests: The authors have no conflict of interest to report., (© 2021 – The authors. Published by IOS Press.)

The purpose of this study was to investigate cognitive functioning by administering the Virtual Supermarket (VSM) test in patients with amnestic mild cognitive impairment (aMCI, N = 37) and age and education-matched healthy controls (HCs, N = 52). An extensive neuropsychological test battery and the VSM were administered to all participants. The aMCI group exhibited lower performance and required more time to complete the VSM compared to HCs. Also, aMCI-Multiple Domain (aMCI-MD) patients performed worse in the "Correct Types", "Correct Quantities", "Bought Unlisted", "Correct Money" variables compared to HCs. Moreover, aMCI-SD patients displayed lower performance in "Bought Unlisted" and "Correct Money" variables compared to HCs. The VSM variables correlated with established neuropsychological test scores. The VSM test was found to discriminate between aMCI and HCs with a correct classification rate (CCR) of 81%. This is a preliminary study showing that the VSM is a valid, brief and user-friendly test. .