39 research outputs found

    An IoT-aware AAL System to Capture Behavioral Changes of Elderly People

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    The ageing of population is a phenomenon that is affecting the majority of developed countries around the world and will soon affect developing economies too. In recent years, both industry and academia are focused on the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. In this context, the behavioral analysis of elderly people can help to prevent the occurrence of Mild Cognitive Impairment (MCI) and frailty problems. The innovative technologies enabling the Internet of Things (IoT) can be used in order to capture personal data for automatically recognizing changes in elderly people behavior in an unobtrusive, low-cost and low-power modality. This work aims to describe the ongoing activities within the City4Age project, funded by the Horizon 2020 Programme of the European Commission, mainly focused on the use of IoT technologies to develop an innovative AAL system able to capture personal data of elderly people in their home and city environments. The proposed architecture has been validated through a proof-of-concept focused mainly on localization issues, collection of ambient parameters, and user-environment interaction aspects

    On the error statistics of turbo decoding for hybrid concatenated codes design

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    In this paper we propose a model for the generation of error patterns at the output of a turbo decoder using a Context Tree based modelling technique. This model can be used not only to generate the decoder error pattern behaviour with little effort, avoiding simulations, but also to investigate \u2013 with no need of performing neither a turbo code distance spectrum analysis, nor the probabilistic characterization of log-likelihood ratios or of the extrinsic information at a turbo decoder output \u2013 the performance of hybrid concatenated coding (HCC) schemes having a turbo code as component code. These coding schemes combine the features of parallel and serially concatenated codes and thus offer more freedom in code design. It has been demonstrated, in fact, that HCCs can perform closer to capacity than serially concatenated codes while still maintaining a minimum distance that grows linearly with block length

    Спосіб автоматизованого пошуку цільових об’єктів на відео з БПЛА в режимі пост-обробки

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    Data obtaining tasks for correct and relevant management decision making pusposes are important. Particularly, in military and rescue areas such data is videodata from an unmanned aircraft vehicle (UAV) camera obtained during its flight over a territory-of-interest. In this case large size of obtained data means a significant problem because of complicating of its manual processing by operator (expert). In addition, data availability must be provided. In practice, the mentioned task is usually solved by recording target videodata onboard during the UAV flight followed by recorded videodata processing after the UAV landing e.g. on the groung control station, i.e. in offline mode. It is obviously to see that using this technique doesn’t solve the problem of complicated target data processing due to manual approach. As for automation of target data processing, as practice shows, every object detection method can potentially decrease processing time, but cannot increase processing quality in comparison with manual processing by operator (expert). Thus, task of ensuring an appropriate balance between availability of target data (videodata from UAV), automation and quality of its processing is relevant. This article i) proposes the technique for automated target object search in videodata from reconnaissance UAVs in post-processing mode by using an adaptive suspicious object search method as an automatic part, ii) describes the corresponding program implementation on C++ for detection method, C# for the user interface part and [standard] platform invoke technique for using the first code (C++) inside the last (C#), iii) shows quantitative characteristics calculated on the set of test videodata. The proposed technique is considered as an appropriate way to solve the specified task.Актуальными вопросами в сфере управления являются вопросы получения необходимых для принтия своевременного и корректного управленческого решения данных. В частности, для военных задач и спасательных операций в качестве таких данных могут выступать результаты проведения воздушной разведки – видеоданные с камеры беспилотного воздушного судна (БпЛА), полученные во время полёта над территорией, представляющей интерес. В этом случае, как показывает практика, существенной проблемой является значительный объём получаемых данных, что усложняет их обработку операторами (экспертами) в ручном режиме. При этом вопросы обеспечения доступности целевых данных также должны быть решены. Таким образом, актуальной задачей является обеспечение приемлемого баланса между доступностью целевых данных (видеоданных – результатов проведения воздушной разведки), оперативностью и качеством их обработки. В данной публикации для решения указанной задачи предлагается способ автоматизированного поиска целевых объектов на видеоданных с разведывательных БпЛА в режиме пост-обработки с использованием адаптивного метода поиска подозрительных объектов в качестве автоматической части.Актуальними питаннями у сфері управління є питання отримання даних, необхідних для прийняття коректного і своєчасного управлінського рішення. Зокрема для військових або рятувальних задач у ролі таких даних можуть виступати результати проведення повітряної розвідки – відеодані з камери безпілотного повітряного судна (БпЛА), отримані під час польоту над територією, що представляє інтерес. В такому випадку, як показує практика, суттєвою проблемою є значний обсяг отримуваних даних, що ускладнює їх обробку операторами (експертами) у ручному режимі. При цьому завдання забезпечення доступності цільових даних також повинні вирішуватись. Отже, актуальною задачею є забезпечення прийнятного балансу між доступністю цільових даних (відеоданих – результатів проведення повітряної розвідки), оперативністю та якістю їх обробки. В даній публікації для вирішення зазначеної задачі пропонується спосіб автоматизованого пошуку цільових об’єктів на відеоданих з розвідувальних БпЛА в режимі пост-обробки із використанням адаптивного метода пошуку підозрілих об’єктів у якості автоматичної частини

    An IoT-Aware Approach for Elderly-Friendly Cities

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    The ever-growing life expectancy of people requires the adoption of proper solutions for addressing the particular needs of elderly people in a sustainable way, both from service provision and economic point of view. Mild cognitive impairments and frailty are typical examples of elderly conditions which, if not timely addressed, can turn out into more complex diseases that are harder and costlier to treat. Information and communication technologies, and in particular Internet of Things technologies, can foster the creation of monitoring and intervention systems, both on an ambient-assisted living and smart city scope, for early detecting behavioral changes in elderly people. This allows to timely detect any potential risky situation and properly intervene, with benefits in terms of treatment's costs. In this context, as part of the H2020-funded City4Age project, this paper presents the data capturing and data management layers of the whole City4Age platform. In particular, this paper deals with an unobtrusive data gathering system implementation to collect data about daily activities of elderly people, and with the implementation of the related linked open data (LOD)-based data management system. The collected data are then used by other layers of the platform to perform risk detection algorithms and generate the proper customized interventions. Through the validation of some use-cases, it is demonstrated how this scalable approach, also characterized by unobtrusive and low-cost sensing technologies, can produce data with a high level of abstraction useful to define a risk profile of each elderly person

    On the Error Statistics of Turbo Decoding for Hybrid Concatenated Codes Design

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    In this paper we propose a model for the generation of error patterns at the output of a turbo decoder. One of the advantages of this model is that it can be used to generate the error sequence with little effort. Thus, it provides a basis for designing hybrid concatenated codes (HCCs) employing the turbo code as inner code. These coding schemes combine the features of parallel and serially concatenated codes and thus offer more freedom in code design. It has been demonstrated, in fact, that HCCs can perform closer to capacity than serially concatenated codes while still maintaining a minimum distance that grows linearly with block length. In particular, small memory-one component encoders are sufficient to yield asymptotically good code ensembles for such schemes. The resulting codes provide low complexity encoding and decoding and, in many cases, can be decoded using relatively few iterations

    An IoT-Aware Architecture for Collecting and Managing Data Related to Elderly Behavior

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    Enhancing Cross-Subject Motor Imagery Classification in EEG-Based Brain–Computer Interfaces by Using Multi-Branch CNN

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    © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).A brain–computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development of BCIs based on EEG is the classification of subject-independent motor imagery data since EEG data are very individualized. Deep learning techniques such as the convolutional neural network (CNN) have illustrated their influence on feature extraction to increase classification accuracy. In this paper, we present a multi-branch (five branches) 2D convolutional neural network that employs several hyperparameters for every branch. The proposed model achieved promising results for cross-subject classification and outperformed EEGNet, ShallowConvNet, DeepConvNet, MMCNN, and EEGNet_Fusion on three public datasets. Our proposed model, EEGNet Fusion V2, achieves 89.6% and 87.8% accuracy for the actual and imagined motor activity of the eegmmidb dataset and scores of 74.3% and 84.1% for the BCI IV-2a and IV-2b datasets, respectively. However, the proposed model has a bit higher computational cost, i.e., it takes around 3.5 times more computational time per sample than EEGNet_Fusion.Peer reviewe

    A critical analysis of an IoT—aware AAL system for elderly monitoring

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    Abstract A growing number of elderly people (65+ years old) are affected by particular conditions, such as Mild Cognitive Impairment (MCI) and frailty, which are characterized by a gradual cognitive and physical decline. Early symptoms may spread across years and often they are noticed only at late stages, when the outcomes remain irrevocable and require costly intervention plans. Therefore, the clinical utility of early detecting these conditions is of substantial importance in order to avoid hospitalization and lessen the socio-economic costs of caring, while it may also significantly improve elderly people's quality of life. This work deals with a critical performance analysis of an Internet of Things aware Ambient Assisted Living (AAL) system for elderly monitoring. The analysis is focused on three main system components: (i) the City-wide data capturing layer, (ii) the Cloud-based centralized data management repository, and (iii) the risk analysis and prediction module. Each module can provide different operating modes, therefore the critical analysis aims at defining which are the best solutions according to context's needs. The proposed system architecture is used by the H2020 City4Age project to support geriatricians for the early detection of MCI and frailty conditions

    Automatic cognitive fatigue detection using wearable fNIRS and machine learning

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    Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtru sively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67%. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.info:eu-repo/semantics/publishedVersio
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