High accuracy context recovery using clustering mechanisms

This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point id and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing state-of-the-art probabilistic clustering technique, the Latent Dirichiet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.<br /

Design and Implementation of System Components for Radio Frequency Based Asset Tracking Devices to Enhance Location Based Services. Study of angle of arrival techniques, effects of mutual coupling, design of an angle of arrival algorithm, design of a novel miniature reconfigurable antenna optimised for wireless communication systems

The angle of arrival estimation of multiple sources plays a vital role in the field of array signal processing as MIMO systems can be employed at both the transmitter and the receiver end and the system capacity, reliability and throughput can be significantly increased by using array signal processing. Almost all applications require accurate direction of arrival (DOA) estimation to localize the sources of the signals. Another important parameter of localization systems is the array geometry and sensor design which can be application specific and is used to estimate the DOA. In this work, various array geometries and arrival estimation algorithms are studied and then a new scheme for multiple source estimation is proposed and evaluated based on the performance of subspace and non-subspace decomposition methods. The proposed scheme has shown to outperform the conventional Multiple Signal Classification (MUSIC) estimation and Bartlett estimation techniques. The new scheme has a better performance advantage at low and high signal to noise ratio values (SNRs). The research work also studies different array geometries for both single and multiple incident sources and proposes a geometry which is cost effective and efficient for 3, 4, and 5 antenna array elements. This research also considers the shape of the ground plane and its effects on the angle of arrival estimation and in addition it shows how the mutual couplings between the elements effect the overall estimation and how this error can be minimised by using a decoupling matrix. At the end, a novel miniaturised multi element reconfigurable antenna to represent the receiver base station is designed and tested. The antenna radiation patterns in the azimuth angle are almost omni-directional with linear polarisation. The antenna geometry is uniplanar printed logspiral with striplines feeding network and biased components to improve the impedance bandwidth. The antenna provides the benefit of small size, and re-configurability and is very well suited for the asset tracking applications

Situation inference and context recognition for intelligent mobile sensing applications

The usage of smart devices is an integral element in our daily life. With the richness of data streaming from sensors embedded in these smart devices, the applications of ubiquitous computing are limitless for future intelligent systems. Situation inference is a non-trivial issue in the domain of ubiquitous computing research due to the challenges of mobile sensing in unrestricted environments. There are various advantages to having robust and intelligent situation inference from data streamed by mobile sensors. For instance, we would be able to gain a deeper understanding of human behaviours in certain situations via a mobile sensing paradigm. It can then be used to recommend resources or actions for enhanced cognitive augmentation, such as improved productivity and better human decision making. Sensor data can be streamed continuously from heterogeneous sources with different frequencies in a pervasive sensing environment (e.g., smart home). It is difficult and time-consuming to build a model that is capable of recognising multiple activities. These activities can be performed simultaneously with different granularities. We investigate the separability aspect of multiple activities in time-series data and develop OPTWIN as a technique to determine the optimal time window size to be used in a segmentation process. As a result, this novel technique reduces need for sensitivity analysis, which is an inherently time consuming task. To achieve an effective outcome, OPTWIN leverages multi-objective optimisation by minimising the impurity (the number of overlapped windows of human activity labels on one label space over time series data) while maximising class separability. The next issue is to effectively model and recognise multiple activities based on the user&#039;s contexts. Hence, an intelligent system should address the problem of multi-activity and context recognition prior to the situation inference process in mobile sensing applications. The performance of simultaneous recognition of human activities and contexts can be easily affected by the choices of modelling approaches to build an intelligent model. We investigate the associations of these activities and contexts at multiple levels of mobile sensing perspectives to reveal the dependency property in multi-context recognition problem. We design a Mobile Context Recognition System, which incorporates a Context-based Activity Recognition (CBAR) modelling approach to produce effective outcome from both multi-stage and multi-target inference processes to recognise human activities and their contexts simultaneously. Upon our empirical evaluation on real-world datasets, the CBAR modelling approach has significantly improved the overall accuracy of simultaneous inference on transportation mode and human activity of mobile users. The accuracy of activity and context recognition can also be influenced progressively by how reliable user annotations are. Essentially, reliable user annotation is required for activity and context recognition. These annotations are usually acquired during data capture in the world. We research the needs of reducing user burden effectively during mobile sensor data collection, through experience sampling of these annotations in-the-wild. To this end, we design CoAct-nnotate --- a technique that aims to improve the sampling of human activities and contexts by providing accurate annotation prediction and facilitates interactive user feedback acquisition for ubiquitous sensing. CoAct-nnotate incorporates a novel multi-view multi-instance learning mechanism to perform more accurate annotation prediction. It also includes a progressive learning process (i.e., model retraining based on co-training and active learning) to improve its predictive performance over time. Moving beyond context recognition of mobile users, human activities can be related to essential tasks that the users perform in daily life. Conversely, the boundaries between the types of tasks are inherently difficult to establish, as they can be defined differently from the individuals&#039; perspectives. Consequently, we investigate the implication of contextual signals for user tasks in mobile sensing applications. To define the boundary of tasks and hence recognise them, we incorporate such situation inference process (i.e., task recognition) into the proposed Intelligent Task Recognition (ITR) framework to learn users&#039; Cyber-Physical-Social activities from their mobile sensing data. By recognising the engaged tasks accurately at a given time via mobile sensing, an intelligent system can then offer proactive supports to its user to progress and complete their tasks. Finally, for robust and effective learning of mobile sensing data from heterogeneous sources (e.g., Internet-of-Things in a mobile crowdsensing scenario), we investigate the utility of sensor data in provisioning their storage and design QDaS --- an application agnostic framework for quality-driven data summarisation. This allows an effective data summarisation by performing density-based clustering on multivariate time series data from a selected source (i.e., data provider). Thus, the source selection process is determined by the measure of data quality. Nevertheless, this framework allows intelligent systems to retain comparable predictive results by its effective learning on the compact representations of mobile sensing data, while having a higher space saving ratio. This thesis contains novel contributions in terms of the techniques that can be employed for mobile situation inference and context recognition, especially in the domain of ubiquitous computing and intelligent assistive technologies. This research implements and extends the capabilities of machine learning techniques to solve real-world problems on multi-context recognition, mobile data summarisation and situation inference from mobile sensing. We firmly believe that the contributions in this research will help the future study to move forward in building more intelligent systems and applications

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Conception et Validation Expérimentale d’un Assistant Numérique pour l’Inclusion Scolaire d’Enfants avec Troubles du Spectre Autistique en Classe Ordinaire

School inclusion of children with Autism Spectrum Disorders (ASD) inmainstream classrooms remains dramatically limited in France, even though it hasbeen recognized as critical for socio- professional perspectives. In fact, the atypicalcognitive functioning, associated with socio-adaptive behavior difficulties(communication, social skills, autonomy, etc.), are usually confronted to nor- malizedexpectations in these mainstream environments, such as schools. New technologiescan be seen as promising levers to overcome the barriers of school inclusion.However, despite a plethoric offer of technologies for children with ASD, scientificstudies are lacking to establish their efficacy, as well as the relevance of their design.This work presents the design and validation of mobile applications to support schoolinclusion of children with ASD in mainstream classrooms through three studies. Thefirst study presents design principles for assistive applications addressing schoolroutines and verbal communication activities of children with ASD; these applicationsare to be used in situ. Combining a user-centered approach and pilot clinical research,the second study presents design principles and experimental validation of an emotionregulation application targeting children with ASD in mainstream classroom. Theresults reveal benefits on self-regulation behaviors, as well as underpinning sociocognitiveprocesses. Finally, in across-syndrome approach, the third study presents theresults of a global intervention, based on cognitive assistive and rehabilitationapplications, involving 48 children and supporting the first inclusion in mainstreamclassrooms of children with ASD and children without ASD (with IntellectualDisabilities or learning disabilities). Benefits are reported for both equipped groups interms of socio-adaptive behaviors, social response and socio-cognitive functioning.Larger benefits have been observed for equipped children with ASD, revealing therelevance of Collège+ intervention for this population.A systemic approach to designing and experimenting mobile applications allowed forimprove- ments in socio-adaptive behaviors and socio-cognitive functioning, crucialfor the success of main- stream school inclusion. Such approach seems promising tosupport school inclusion of children with ASD in mainstream classrooms, and offersbroad perspectives by enriching contents, designing new applications as well asexperimenting validation methodologies for mainstream environments.Bien que reconnue comme critique pour le devenir socio-professionnel desenfants avec Troubles du Spectre Autistique (TSA), l’inclusion scolaire en classeordinaire demeure en France peu acces- sible pour ce public. En effet, lefonctionnement cognitif atypique associé aux limitations des comportements socioadaptatifs(communication, socialisation, autonomie etc.), se heurte bien souvent auxconditions normées des milieux ordinaires tels que l’école. Les nouvelles technologiessont aujourd’hui pressenties comme leviers prometteurs pour surmonter ces barrières àl’inclusion scolaire. Cependant, malgré un marché pléthorique de technologies ciblantles TSA, les études scientifiques manquent pour statuer sur leur efficacité mais aussisur les fondements mêmes de leur conception.Ce travail présente la conception et la validation d’applications mobiles pourl’inclusion scolaire d’enfants avec TSA en classe ordinaire au travers de trois études.Dans une approche centrée- utilisateur, l’Étude 1 présente des principes de conceptiond’applications d’assistance aux activités de classe et activités communicationnelles desenfants avec TSA pour une utilisation in situ. Dans une approche centrée utilisateur etde recherche clinique pilote, l’Étude 2 présente les principes de conception et lavalidation expérimentale d’une application d’assistance à la régulation émotionnelledes enfants TSA en classe ordinaire. Les résultats indiquent des bénéfices sur lescomportements d’auto-régulation ainsi que sur les processus sociocognitifs sousjacents.Enfin, dans une approche cross-syndromes, l’Étude 3 présente les résultatsd’une intervention globale reposant sur des ap- plications d’assistance et deremédiation cognitives (dispositif Collège+) déployées auprès de 48 enfants et visant àsoutenir la primo-inclusion en classe ordinaire d’enfants avec TSA et d’enfants nonTSA (avec Déficiences intellectuelles ou troubles globaux de l’apprentissage). Desbénéfices sont rapportés pour tous les enfants équipés en termes de comportementssocio-adaptatifs, de réponse sociale et de fonctionnement sociocognitif. Aussi, de pluslarges bénéfices sont observés pour les enfants TSA révélant ainsi la pertinence del’intervention Collège+ pour le public avec TSA.En conclusion, un approche systémique dans la conception et l’expérimentationd’applications mobiles a permis des améliorations dans l’adaptation descomportements et du fonctionnement socio-cognitif, cruciaux dans la réussite d’uneinclusion scolaire en classe ordinaire. Cette approche semble donc prometteuse poursoutenir l’inclusion scolaire en milieu ordinaire des enfants avec TSA, et offre delarges perspectives de travail, tant sur l’enrichissement des contenus, la conception denouvelles applications que des méthodes de validation expérimentale