890 research outputs found

    Human behavioural analysis with self-organizing map for ambient assisted living

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    This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints

    Security and Privacy Issues in IoT Healthcare Application for Disabled Users in Developing Economies

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    In this paper, we explore the security and privacy issues of Internet of Things (IoT) healthcare applications for special needs users. IoT enables health-related organizations to lift important data from diverse sources in real-time and this helps in precise decision-making. The transformation of the health sector, required enhancement and efficiency of protective systems, thereby reducing data vulnerability and hence, providing opportunities for secure patient data, particularly, for special needs patients. A quantitative method for purposive sampling technique was adopted and eighty-eight respondents provided the process of how the IoT technology was utilised. Data findings indicated that IoT monitoring devices have the detective ability for a person with special needs living alone with problems related to vital signs of diseases or disabilities. Personal patient health records are integrated into the e-health Centre via IoT technologies. For data privacy, security, and confidentiality, patients' records are kept on Personal Health Record Systems (PHRS). The research revealed suspected breaches of information due to cyber-attacks on the probability of false data errors in the PHRS, leading to special needs personal data leakage

    Supporting independent living for older adults; employing a visual based fall detection through analysing the motion and shape of the human body

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    Falls are one of the greatest risks for older adults living alone at home. This paper presents a novel visual-based fall detection approach to support independent living for older adults through analysing the motion and shape of the human body. The proposed approach employs a new set of features to detect a fall. Motion information of a segmented silhouette when extracted can provide a useful cue for classifying different behaviours, while variation in shape and the projection histogram can be used to describe human body postures and subsequent fall events. The proposed approach presented here extracts motion information using best-fit approximated ellipse and bounding box around the human body, produces projection histograms and determines the head position over time, to generate 10 features to identify falls. These features are fed into a multilayer perceptron neural network for fall classification. Experimental results show the reliability of the proposed approach with a high fall detection rate of 99.60% and a low false alarm rate of 2.62% when tested with the UR Fall Detection dataset. Comparisons with state of the art fall detection techniques show the robustness of the proposed approach

    Structural Topology Optimization: Moving Beyond Linear Elastic Design Objectives

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    Topology optimization is a systematic, free-form approach to the design of structures. It simultaneously optimizes material quantities and system connectivity, enabling the discovery of new, high-performance structural concepts. While powerful, this design freedom has a tendency to produce solutions that are unrealizable or impractical from a structural engineering perspective. Examples include overly complex topologies that are expensive to construct and ultra-slender subsystems that may be overly susceptible to imperfections. This paper summarizes recent tools developed by the authors capable of mitigating these shortcomings through consideration of (1) constructability, (2) nonlinear mechanics, and (3) uncertainties

    Activity Recognition and Prediction in Real Homes

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    In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201

    Performances of PA hollow fiber membrane with the CTA flat sheet membrane for forward osmosis process

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    © 2013, © 2013 Balaban Desalination Publications. All rights reserved. Abstract: Fertilizer drawn forward osmosis desalination has been earlier explored using flat sheet forward osmosis (FSFO) membrane, which highlighted flux and reverse solute flux (RSF) performance. This study evaluated and compared the performances of a newly developed polyamide (PA)-based hollow fiber forward osmosis (HFFO) membrane and cellulose triacetate FSFO membrane. Both membranes were evaluated for pure water permeability, salt rejection rate (1,000 mg/L NaCl) in RO mode. Physical structure and morphology were further examined using scanning electron micrograph (SEM). SEM images revealed that the overall thickness of the HFFO and FSFO membranes was 152 and 91 μm, respectively. Flux and RSF performances of these two membranes were evaluated using nine fertilizer DS as NH4Cl, KNO3, KCl, (NH4)2SO4, Ca(NO3)2, NH4H2PO4, (NH4)2HPO4, NaNO3, and CO(NH2)2 in active layer–feed solution membrane orientation. HFFO membrane clearly showed better performance for water flux with five DS ((NH4)2SO4, NH4H2PO4, KNO3, CO(NH2)2, and NaNO3) as they showed up to 66% increase in flux. Beside thick PA active layer of HFFO membrane, higher water flux outcome for forward osmosis (FO) process further highlighted the significance of the nature of support layer structure, the thickness and surface chemistry of the active layer of the membrane in the FO process. On the other hand, most DS showed lower RSF with HFFO membrane with the exception of Ca(NO3)2. Most of DS having monovalent cation and anions showed significantly lower RSF with HFFO membrane

    Revisiting element removal for density-based structural topology optimization with reintroduction by Heaviside projection

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    We present a strategy grounded in the element removal idea of Bruns and Tortorelli [1] and aimed at reducing computational cost and circumventing potential numerical instabilities of density-based topology optimization. The design variables and the relative densities are both represented on a fixed, uniform finite element grid, and linked through filtering and Heaviside projection. The regions in the analysis domain where the relative density is below a specified threshold are removed from the forward analysis and replaced by fictitious nodal boundary conditions. This brings a progressive cut of the computational cost as the optimization proceeds and helps to mitigate numerical instabilities associated with low-density regions. Removed regions can be readily reintroduced since all the design variables remain active and are modeled in the formal sensitivity analysis. A key feature of the proposed approach is that the Heaviside functions promote material reintroduction along the structural boundaries by amplifying the magnitude of the sensitivities inside the filter reach. Several 2D and 3D structural topology optimization examples are presented, including linear and nonlinear compliance minimization, the design of a force inverter, and frequency and buckling load maximization. The approach is shown to be effective at producing optimized designs equivalent or nearly equivalent to those obtained without the element removal, while providing remarkable computational savings
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