What Do Family Caregivers of Alzheimer's Disease Patients Desire in Smart Home Technologies?

Objectives - The authors' aim was to investigate the representations, wishes, and fears of family caregivers (FCs) regarding 14 innovative technologies (IT) for care aiding and burden alleviation, given the severe physical and psychological stress induced by dementia care, and the very slow uptake of these technologies in our society. Methods - A cluster sample survey based on a self-administered questionnaire was carried out on data collected from 270 families of patients with Alzheimer's disease or related disorders, located in the greater Paris area. Multiple Correspondence Analysis was used in addition to usual statistical tests to identify homogenous FCs clusters concerning the appreciation or rejection of the considered technologies. Results - Two opposite clusters were clearly defined: FCs in favor of a substantial use of technology, and those rather or totally hostile. Furthermore the distributions of almost all the answers of appreciations were U shaped. Significant relations were demonstrated between IT appreciation and FC's family or gender statuses (e.g., female FCs appreciated more than male FCs a tracking device for quick recovering of wandering patients: p=0.0025, N=195). Conclusions - The study provides further evidence of the contrasted perception of technology in dementia care at home, and suggests the development of public debates based on rigorous assessment of practices and a strict ethical aim to protect against misuse

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Crowdsourcing based Room Localization on Smartphones

People spend approximately 90% of their time indoors, and human indoor activities are strongly correlated with the rooms they are in. Room localization, which identifies the room a person or smartphone is in, provides a powerful tool for characterizing human indoor activities and helping address challenges in public health, productivity, building management, etc. Designing a room localization system that is practically useful in real-world environments is challenging. First, due to the complex multi-path propagation problem, Wi-Fi signals obtained by smartphones are dynamic and noisy. Such noise obscures the unique relationship between Wi-Fi signals and individual rooms. Second, existing room localization methods require labor-intensive manual annotation of individual rooms. The process is time-consuming and expensive, which is a key limitation of existing room localization applications. Third, knowledge of indoor floorplans is often required by room localization applications. However, indoor floorplans are either unavailable or obtaining them requires slow, tedious, and error-prone manual labor. In addition, the overhead of room localization, e.g., energy consumption, imposed on personal smartphones must be low. To tackle those challenges, this thesis proposed a set of techniques: (1) an accurate temporal n-gram augmented Bayesian room positioning method that leverages the ordered sequence information of access points and users' daily motion pattern among rooms; (2) an automatic room fingerprinting approach that identifies in-room occupancy ``hotspot(s)" using density of Wi-Fi signals, and then learns the inter-zone correlation -- thereby distinguishing different rooms; (3) an automatic floorplan construction method that determines the geometries of individual rooms, as well as room adjacency information, and then constructs an indoor floorplan through hallway layout learning and force directed dilation; and (4) an energy-efficient trip detection framework that consists of two modes: the deliberation mode learns cell-id patterns using GPS/Wi-Fi based localization methods, and the intuition mode only uses cell-ids and learned patterns for trip detection

Geographical places as a personalisation element: extracting profiles from human activities and services of visited places in mobility logs

Collecting personal mobility traces of individuals is currently applicable on a large scale due to the popularity of position-aware mobile phones. Statistical analysis of GPS data streams, collected with a mobile phone, can reveal several interesting measures such as the most frequently visited geographical places by some individual. Applying probabilistic models to such data sets can predict the next place to visit, and when. Several practical applications can utilise the results of such analysis. Current state of the art, however, is limited in terms of the qualitative analysis of personal mobility logs. Without explicit user-interactions, not much semantics can be inferred from a GPS log. This work proposes the utilisation of the common human activities and services provided at certain place types to extract semantically rich profiles from personal mobility logs. The resulting profiles include spatial, temporal and generic thematic description of a user. The work introduces several pre-processing methods for GPS data streams, collected with personal mobile devices, which improved the quality of the place extraction process from GPS logs. The thesis also introduces a method for extracting place semantics from multiple data sources. A textual corpus of functional descriptions of human activities and services associated with certain geographic place types is analysed to identify the frequent linguistic patterns used to describe such terms. The patterns found are then matched against multiple textual data sources of place semantics, to extract such terms, for a collection of place types. The results were evaluated in comparison to an equivalent expert ontology, as well as to semantics collected from the general public. Finally, the work proposes a model for the resulting profiles, the necessary algorithms to build and utilise such profiles, along with an encoding mark-up language. A simulated mobile application was developed to show the usability and for evaluation of the resulting profiles