This work addresses the challenge of analysing the quality of human movements from visual information which has use in a broad range of applications, from diagnosis and rehabilitation to movement optimisation in sports science. Traditionally, such assessment is performed as a binary classification between normal and abnormal by comparison against normal and abnormal movement models, e.g. [5]. Since a single model of abnormal movement cannot encompass the variety of abnormalities, another class of methods only compares against one model of normal movement, e.g. [4]. We adopt this latter strategy and propose a continuous assessment of movement quality, rather than a binary classification, by quantifying the deviation from a normal model. In addition, while most methods can only analyse a movement after its completion e.g. [6], this assessment is performed on a frame-by-frame basis in order to allow fast system response in case of an emergency, such as a fall.Methods such as [4,6] are specific to one type of movement, mostly due to the features used. In this work, we aim to represent a large variety of movements by exploiting full body information. We use a depth camera and a skeleton tracker [3] to obtain the position of the main joints of the body, as seen in Fig. 1. We normalise this skeleton for global position and orientation of the camera, and for the varying height of the subjects, e.g. using Procrustes analysis.The normalised skeletons have high dimensionality and tend to contain outliers. Thus, the dimensionality is reduced using Diffusion Maps [1] which is modified by including the extension that Gerber et al. [2] presented to deal with outliers in Laplacian Eigenmaps. The resulting high level feature vector Y, obtained from the normalised skeleton at one frame, represents an individual pose and is used to build a statistical model of normal movement.Our statistical model is made up of two components that describe the normal poses and the normal dynamics of the movement. The pose model is in the form of the probability density function (pdf) f Y (y) of a random variable Y that takes as value y = Y our pose feature vector Y. The pdf is learnt from all the frames of training sequences that contain normal instances of the movement, using a Parzen window estimator. The quality of a new pose y t at frame t is then assessed as the log-likelihood of being described by the pose model, i.e. llh pose = log f Y (y t ) .(The dynamics model is represented as the pdf f Y t (y t |y 1 , . . . , y t−1 ) which describes the likelihood of a pose y t at a new frame t given the poses at the previous frames. In order to compute it, we introduce X t with value x t ∈ [0, 1], which is the stage of the (periodic or non-periodic) movement at frame t. Note, in the case of periodic movements, this movement stage can also be seen as the phase of the movement's cycle. Based on Markovian assumptions, we find thatwithx t an approximation of x t that minimises f {X 0 ,...,X t } (x 0 , . . . , x t |y 1 , . . . , y t ). f Y t (y t |x t ) is...

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The recent surge in popularity of real-time RGB-D sensors has encouraged research into combining colour and depth data for tracking. The results from a few, recent works in RGB-D tracking have demonstrated that state-of-the-art RGB tracking algorithms can be outperformed by approaches that fuse colour and depth, for example [1,3,4,5].In this paper, we propose a real-time RGB-D tracker which we refer to as the Depth Scaling Kernalised Correlations Filters (DS-KCF). It is based on, and improves upon, the RGB Kernelised Correlation Filters tracker (KCF) from [2]. KCF is based on the use of the 'kernel trick' to extend correlation filters for very fast RGB tracking. The KCF tracker has important characteristics, in particular its ability to combine high accuracy and processing speed as demonstrated in [2,6]. It is based on a simple processing chain that comprises training, detection, retraining and model update obtained by linear interpolation. The key to KCF is that it exploits the properties of circulant matrices to achieve efficient learning by implicitly encoding convolution and by allowing to operate in the Fourier domain using mainly element wise operations.The proposed DS-KCF tracker 1 extends the RGB KCF tracker in three ways: (i) we employ an the efficient combination of colour and depth features (ii) we propose an efficient a novel management of scale changes and (iii) occlusions handling. The improvements we implement provide higher rates of accuracy while still operating at better than real-time frame rates (35fps on average ). In particular, depth data in the target region is segmented with a fast K-means approach to extract relevant features for the target's depth distribution. Modelled as a Gaussian distribution, this data allows to identify scale changes and efficiently model them in the Fourier domain. The advantage of the proposed approach is that only a single target model is kept and updated. Furthermore, region depth distribution enables the detection of possible occlusions identified as sudden changes in the target region's depth histogram, and recovering lost tracks by searching for the unoccluded object in specifically identified key areas. During an occlusion, the model is not updated and the occluding object is tracked to guide the target's search space.

Ambient Assisted Living (AAL) systems based on sensor technologies are seen as key enablers to an ageing society. However, most approaches in this space do not provide a truly generic ambient space -one that is not only capable of assisting people with diverse medical conditions, but can also recognise the habits of healthy habitants, as well as those with developing medical conditions. The recognition of Activities of Daily Living (ADL) is key to the understanding and provisioning of appropriate and efficient care. However, ADL recognition is particularly difficult to achieve in multi-resident spaces; especially with single-mode (albeit carefully crafted) solutions, which only have limited capabilities. To address these limitations we propose a multi-modal system architecture for AAL remote healthcare monitoring in the home, gathering information from multiple, diverse (sensor) data sources. In this paper we report on developments made to-date in various technical areas with respect to critical issues such as cost, power consumption, scalability, interoperability and privacy.Index Terms-Ambient Intelligence, Ambient Assisted Living, eHealth, Internet of Things IEEE ICC 2015 -Workshop on ICT-enabled services and technologies for eHealth and Ambient Assisted Living 978-1-4673-6305-1/15/$31.00 ©2015 IEEE

We propose an RGB-D single-object tracker, built upon the extremely fast RGB-only KCF tracker that is able to exploit depth information to handle scale changes, occlusions, and shape changes. Despite the computational demands of the extra functionalities, we still achieve realtime performance rates of 35-43 fps in MATLAB and 187 fps in our C?? implementation. Our proposed method includes fast depth-based target object segmentation that enables, (1) efficient scale change handling within the KCF core functionality in the Fourier domain, (2) the detection of occlusions by temporal analysis of the target's depth distribution, and (3) the estimation of a target's change of shape through the temporal evolution of its segmented silhouette allows. Finally, we provide an in-depth analysis of the factors affecting the throughput and precision of our proposed tracker and perform extensive comparative analysis. Both the MATLAB and C?? versions of our software are available in the public domain.