Effectiveness of an Ambient Assisted Living (HomeAssist) Platform for Supporting Aging in Place of Older Adults With Frailty: Protocol for a Quasi-Experimental Study

International audienceBackground: Ambient assisted living (AAL) technologies are viewed as a promising way to prolong aging in place, particularly when they are designed as closely as possible to the needs of the end users. However, very few evidence-based results have been provided to support its real value, notably for frail older adults who have a high risk of autonomy loss as well as entering a nursing home. Objective: We hypothesized that the benefit from an AAL with a user-centered design is effective for aging in place for frail older adults in terms of everyday functioning (instrumental activities of daily-life scale). In addition, our secondary hypotheses are that such an AAL decreases or neutralizes the frailty process and reduces the rates of institutionalization and hospitalization and that it improves the psychosocial health of participants and their caregivers when compared with the control condition. We also assume that a large proportion of equipped participants will have a satisfactory experience and will accept a subscription to an internet connection to prolong their participation. Methods: HomeAssist (HA) is an AAL platform offering a large set of apps for 3 main age-related need domains (activities of daily-living, safety, and social participation), relying on a basic set of entities (sensors, actuators, tablets, etc). The HA intervention involves monitoring based on assistive services to support activities related to independent living at home. The study design is quasi-experimental with a duration of 12 months, optionally extensible to 24 months. Follow-up assessments occurred at 0, 12, and 24 months. The primary outcome measures are related to everyday functioning. Secondary outcome measures include indices of frailty, cognitive functioning, and psychosocial health of the participants and their caregivers. Every 6 months, user experience and attitudes toward HA are also collected from equipped participants. Concomitantly, data on HA use will be collected. All measures of the study will be tested based on an intention-to-treat approach using a 2-tailed level of significance set at α=.05, concerning our primary and secondary efficacy outcomes. Results: Descriptive analyses were conducted to characterize the recruited equipped participants compared with the others (excluded and refusals) on the data available at the eligibility visit, to describe the characteristics of the recruited sample at baseline, as well as those of the dropouts. Finally, recruitment at 12 months included equipped participants (n=73), matched with control participants (n=474, from pre-existing cohorts). The results of this study will be disseminated through scientific publications and conferences. This will provide a solid basis for the creation of a start-up to market the technology. Conclusions: This trial will inform the real-life efficacy of HA in prolonging aging in place for frail older adults and yield an informed analysis of AAL use and adoption in frail older individuals

A Bluetooth 5 Opportunistic Edge Computing System for Vehicular Scenarios

[Abstract]: The limitations of many IoT devices in terms of storage, computing power and energy consumption require them to be connected to other devices when performing computationally intensive tasks, as happens with IoT systems based on edge computing architectures. However, the lack of wireless connectivity in the places where IoT nodes are deployed or through which they move is still a problem. One of the solutions to mitigate this problem involves using opportunistic networks, which provide connectivity and processing resources efficiently while reducing the communications traffic with remote clouds. Thus, opportunistic networks are helpful in situations when wireless communication coverage is not available, as occurs in certain rural areas, during natural disasters, in wars or when other factors cause network disruptions, as well as in other IoT scenarios in which the cloud becomes saturated (for example, due to an excessive amount of concurrent communications or when denial-of-service (DoS) attacks occur). This article presents the design and initial validation of a novel opportunistic edge computing (OEC) system based on Bluetooth 5 and the use of low-cost single-board computers (SBCs). After describing the proposed OEC system, experimental results are presented for a real opportunistic vehicular IoT scenario. Specifically, the latency and packet loss are measured thanks to the use of an experimental testbed made of two separate IoT networks (each conformed by an IoT node and an OEC gateway): one located in a remote office and another one inside a moving vehicle, which was driven at different vehicular speeds. The obtained results show average latencies ranging from 716 to 955 ms with packet losses between 7% and 27%. As a result, the developed system is useful for providing opportunistic services to moving IoT nodes with relatively low latency requirements.Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G 2019/01Ministerio de Ciencia e Innovación; PID2020-118857RA-I0

A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms

Classifying human gestures using surface electromyografic sensors (sEMG) is a challenging task. Wearable sensors have proven to be extremely useful in this context, but their performance is limited by several factors (signal noise, computing resources, battery consumption, etc.). In particular, computing resources impose a limitation in many application scenarios, in which lightweight classification approaches are desirable. Recent research has shown that machine learning techniques are useful for human gesture classification once their salient features have been determined. This paper presents a novel approach for human gesture classification in which two different strategies are combined: a) a technique based on autoencoders is used to perform feature extraction; b) two alternative machine learning algorithms (namely J48 and K*) are then used for the classification stage. Empirical results are provided, showing that for limited computing power platforms our approach outperforms other alternative methodologies.Fil: Micheletto, Matías Javier. Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Universidad Nacional de la Patagonia "San Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge; ArgentinaFil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

Internet of Things for beyond-the-laboratory prosthetics research

Research on upper-limb prostheses is typically laboratory-based. Evidence indicates that research has not yet led to prostheses that meet user needs. Inefficient communication loops between users, clinicians and manufacturers limit the amount of quantitative and qualitative data that researchers can use in refining their innovations. This paper offers a first demonstration of an alternative paradigm by which remote, beyond-the-laboratory prosthesis research according to user needs is feasible. Specifically, the proposed Internet of Things setting allows remote data collection, real-time visualization and prosthesis reprogramming through Wi-Fi and a commercial cloud portal. Via a dashboard, the user can adjust the configuration of the device and append contextual information to the prosthetic data. We evaluated this demonstrator in real-time experiments with three able-bodied participants. Results promise the potential of contextual data collection and system update through the internet, which may provide real-life data for algorithm training and reduce the complexity of send-home trials. This article is part of the theme issue ‘Advanced neurotechnologies: translating innovation for health and well-being’

Neutrosophic geometric distribution: Data generation under uncertainty and practical applications

This paper introduces the geometric distribution in the context of neutrosophic statistics. The research outlines the essential properties of this new distribution and introduces novel algorithms for generating imprecise geometric data. The study explores the practical applications of this distribution in the industry, highlighting differences in data generated under deterministic and indeterminate conditions using detailed tables, simulation studies, and real-world applications. The results indicate that the level of uncertainty has a substantial impact on data generation from the geometric distribution. These findings suggest updating classical statistical algorithms to better handle the generation of imprecise data. Therefore, decision-makers should exercise caution when using data from the geometric distribution in uncertain environments

Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction

The file attached to this record is the author's final peer reviewed version.Sleep behavior is traditionally monitored with polysomnography, and sleep stage patterns are a key marker for sleep quality used to detect anomalies and diagnose diseases. With the growing demand for personalized healthcare and the prevalence of the Internet of Things, there is a trend to use everyday technologies for sleep behavior analysis at home, having the potential to eliminate expensive in-hospital monitoring. In this paper, we conceived a multi-source data mining approach to personalized sleep-wake pattern recog-nition which uses physiological data and personal information to facilitate fine-grained detection. Physiological data includes actigraphy and heart rate variability and personal data makes use of gender, health status and race infor-mation which are known influence factors. Moreover, we developed a personal-ized sleep parameter extraction technique fused with the sleep-wake approach, achieving personalized instead of static thresholds for decision-making. Results show that the proposed approach improves the accuracy of sleep and wake stage recognition, therefore, offers a new solution for personalized sleep-based health monitoring

Biomove: Biometric user identification from human kinesiological movements for virtual reality systems

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Virtual reality (VR) has advanced rapidly and is used for many entertainment and business purposes. The need for secure, transparent and non-intrusive identification mechanisms is important to facilitate users’ safe participation and secure experience. People are kinesiologically unique, having individual behavioral and movement characteristics, which can be leveraged and used in security sensitive VR applications to compensate for users’ inability to detect potential observational attackers in the physical world. Additionally, such method of identification using a user’s kinesiological data is valuable in common scenarios where multiple users simultaneously participate in a VR environment. In this paper, we present a user study (n = 15) where our participants performed a series of controlled tasks that require physical movements (such as grabbing, rotating and dropping) that could be decomposed into unique kinesiological patterns while we monitored and captured their hand, head and eye gaze data within the VR environment. We present an analysis of the data and show that these data can be used as a biometric discriminant of high confidence using machine learning classification methods such as kNN or SVM, thereby adding a layer of security in terms of identification or dynamically adapting the VR environment to the users’ preferences. We also performed a whitebox penetration testing with 12 attackers, some of whom were physically similar to the participants. We could obtain an average identification confidence value of 0.98 from the actual participants’ test data after the initial study and also a trained model classification accuracy of 98.6%. Penetration testing indicated all attackers resulted in confidence values of less than 50% (\u3c50%), although physically similar attackers had higher confidence values. These findings can help the design and development of secure VR systems

Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance

Deep learning-based graffiti detection: A study using Images from the streets of Lisbon

This research work comes from a real problem from Lisbon City Council that was interested in developing a system that automatically detects in real-time illegal graffiti present throughout the city of Lisbon by using cars equipped with cameras. This system would allow a more efficient and faster identification and clean-up of the illegal graffiti constantly being produced, with a georeferenced position. We contribute also a city graffiti database to share among the scientific community. Images were provided and collected from different sources that included illegal graffiti, images with graffiti considered street art, and images without graffiti. A pipeline was then developed that, first, classifies the image with one of the following labels: illegal graffiti, street art, or no graffiti. Then, if it is illegal graffiti, another model was trained to detect the coordinates of graffiti on an image. Pre-processing, data augmentation, and transfer learning techniques were used to train the models. Regarding the classification model, an overall accuracy of 81.4% and F1-scores of 86%, 81%, and 66% were obtained for the classes of street art, illegal graffiti, and image without graffiti, respectively. As for the graffiti detection model, an Intersection over Union (IoU) of 70.3% was obtained for the test set.info:eu-repo/semantics/publishedVersio

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy