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

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

Activity Recognition and Prediction in Real Homes

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

Trading of cloud of things resources

Cloud Computing and Internet of Things (IoT) continue to emerge as revolutionary paradigms to support wide range of real world scenarios. They promise benefits for increasing number of applications, including health, smart cities, smart homes, smart logistics, video surveillance, energy and environmental monitoring. Independent deployments of each technology have issues that can be resolved partially or fully by integrating Cloud and IoT. This integration forms a new paradigm that is called Cloud of Things (CoT)supporting Everything as a Service (XaaS) service model. Despite the issues integration resolves, the integrated services will suffer from issues that Cloud and IoT offerings previously encountered. This includes interoperability, ambiguous SLAs, QoS, elasticity and reliability concerns. This paper argues that commoditising CoT resources will help resolving these issues. This paper aims to; 1) review the state-of-the-art in CoT literature 2) propose a conceptual model for CoT marketplace and its basic trading processes

A multi-level refinement approach towards the classification of quotidian activities using accelerometer data

Wearable inertial measurement units incorporating accelerometers and gyroscopes are increasingly used for activity analysis and recognition. In this paper an activity classification algorithm is presented which includes a novel multi-step refinement with the aim of improving the classification accuracy of traditional approaches. To do so, after the classification takes place, information is extracted from the confusion matrix to focus the computational efforts on those activities with worse classification performance. It is argued that activities differ diversely from each other, therefore a specific set of features may be informative to classify a specific set of activities, but such informativeness should not necessarily be extended to a different activity set. This approach has shown promising results, achieving important classification accuracy improvements of up to 4% with the use of low-dimensional feature vectors

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

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

Thin film composite hollow fibre forward osmosis membrane module for the desalination of brackish groundwater for fertigation

© 2015 Elsevier B.V. The performance of recently developed polyamide thin film composite hollow fibre forward osmosis (HFFO) membrane module was assessed for the desalination of brackish groundwater for fertigation. Four different fertilisers were used as draw solution (DS) with real BGW from the Murray-Darling Basin in Australia. Membrane charge and its electrostatic interactions with ions played a significant role in the performance of the HFFO module using fertiliser as DS. Negatively charged polyamide layer promotes sorption of multivalent cations such as Ca2+ enhancing ion flux and membrane scaling. Inorganic scaling occurred both on active layer and inside the support layer depending on the types of fertiliser DS used resulting in severe flux decline and this study therefore underscores the importance of selecting suitable fertilisers for the fertiliser drawn forward osmosis (FDFO) process. Water flux under active layer DS membrane orientation was about twice as high as the other orientation indicating the need to further optimise the membrane support structure formation. Water flux slightly improved at higher crossflow rates due to enhanced mass transfer on the fibre lumen side. At 45% packing density, HFFO could have three times more membrane area and four times more volumetric flux output for an equivalent 8040 cellulose triacetate flat-sheet FO membrane module

Do residents’ perceptions of being well-placed and objective presence of local amenities match? A case study in West Central Scotland, UK

Background:<p></p> Recently there has been growing interest in how neighbourhood features, such as the provision of local facilities and amenities, influence residents’ health and well-being. Prior research has measured amenity provision through subjective measures (surveying residents’ perceptions) or objective (GIS mapping of distance) methods. The latter may provide a more accurate measure of physical access, but residents may not use local amenities if they do not perceive them as ‘local’. We believe both subjective and objective measures should be explored, and use West Central Scotland data to investigate correspondence between residents’ subjective assessments of how well-placed they are for everyday amenities (food stores, primary and secondary schools, libraries, pharmacies, public recreation), and objective GIS-modelled measures, and examine correspondence by various sub-groups.<p></p> Methods:<p></p> ArcMap was used to map the postal locations of ‘Transport, Health and Well-being 2010 Study’ respondents (n = 1760), and the six amenities, and the presence/absence of each of them within various straight-line and network buffers around respondents’ homes was recorded. SPSS was used to investigate whether objective presence of an amenity within a specified buffer was perceived by a respondent as being well-placed for that amenity. Kappa statistics were used to test agreement between measures for all respondents, and by sex, age, social class, area deprivation, car ownership, dog ownership, walking in the local area, and years lived in current home.<p></p> Results:<p></p> In general, there was poor agreement (Kappa <0.20) between perceptions of being well-placed for each facility and objective presence, within 800 m and 1000 m straight-line and network buffers, with the exception of pharmacies (at 1000 m straight-line) (Kappa: 0.21). Results varied between respondent sub-groups, with some showing better agreement than others. Amongst sub-groups, at 800 m straight-line buffers, the highest correspondence between subjective and objective measures was for pharmacies and primary schools, and at 1000 m, for pharmacies, primary schools and libraries. For road network buffers under 1000 m, agreement was generally poor.<p></p> Conclusion:<p></p> Respondents did not necessarily regard themselves as well-placed for specific amenities when these amenities were present within specified boundaries around their homes, with some exceptions; the picture is not clear-cut with varying findings between different amenities, buffers, and sub-groups

A Model for Computer Supported Learning in Undergraduate Education

Reflecting on an earlier observation by Dr. Herb Thompson (Department of Economics) that most of us had to teach ourselves how to teach* , I realized early on in my career that the best way to describe my role as a teacher is that of a learning facilitator. Sometimes that entails leading the way, and other times it requires pointing directions and getting out of the way

Spontaneous Interlayer Coherence in Double-Layer Quantum Hall Systems: Symmetry Breaking Interactions, In-Plane Fields and Phase Solitons

At strong magnetic fields double-layer two-dimensional-electron-gas systems can form an unusual broken symmetry state with spontaneous inter-layer phase coherence. The system can be mapped to an equivalent system of pseudospin $1/2$ particles with pseudospin-dependent interactions and easy-plane magnetic order. In this paper we discuss how the presence of a weak interlayer tunneling term alters the properties of double-layer systems when the broken symmetry is present. We use the energy functional and equations of motion derived earlier to evaluate the zero-temperature response functions of the double-layer system and use our results to discuss analogies between this system and Josephson-coupled superconducting films. We also present a qualitative picture of the low-energy charged excitations of this system. We show that parallel fields induce a highly collective phase transition to an incommensurate state with broken translational symmetry.Comment: 26 pages, RevTex, 8 postscript figures (submitted to Phys. Rev. B

Wetting transitions of Ne

We report studies of the wetting behavior of Ne on very weakly attractive surfaces, carried out with the Grand Canonical Monte Carlo method. The Ne-Ne interaction was taken to be of Lennard-Jones form, while the Ne-surface interaction was derived from an ab initio calculation of Chizmeshya et al. Nonwetting behavior was found for Li, Rb, and Cs in the temperature regime explored (i.e., T < 42 K). Drying behavior was manifested in a depleted fluid density near the Cs surface. In contrast, for the case of Mg (a more attractive potential) a prewetting transition was found near T= 28 K. This temperature was found to shift slightly when a corrugated potential was used instead of a uniform potential. The isotherm shape and the density profiles did not differ qualitatively between these cases.Comment: 22 pages, 12 figures, submitted to Phys. Rev.