Claudia Villalonga

The SENSEI project: integrating the physical world with the digital world of the network of the future

The Internet extends its reach to the real world through innovations collectively termed the Internet of Things (IoT). The IoT aims at integrating technologies such as radio frequency identification, wireless sensor and actuator networks (WSANs), and networked embedded devices. Recent ideas envision the Internet as an all encompassing infrastructure that connects the physical into the digital world: the real world Internet (RWI). The European project SENSEI plays a leading role within the current efforts to create an underlying architecture and services for the future Internet and to realize the vision of the RWI.

mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications

Mobile health is an emerging field which is attracting much attention. Nevertheless, tools for the development of mobile health applications are lacking. This work presents mHealthDroid, an open source Android implementation of a mHealth Framework designed to facilitate the rapid and easy development of biomedical apps. The framework is devised to leverage the potential of mobile devices like smartphones or tablets, wearable sensors and portable biomedical devices. The framework provides functionalities for resource and communication abstraction, biomedical data acquisition, health knowledge extraction, persistent data storage, adaptive visualization, system management and value-added services such as intelligent alerts, recommendations and guidelines.

Mobile Ontology: Towards a Standardized Semantic Model for the Mobile Domain

Ontologies will be crucial for the future development of Next Generation Service Delivery Platforms. While various projects have defined ontologies for the mobile domain, there is yet little agreement on a common semantic model. One reason is the intrinsically hard problem of finding, using, mapping and evolving already existing ontologies. In this paper we present the Mobile Ontology, an effort within the IST project SPICE to converge towards a standardized ontology. Our approach is based on a minimal core ontology that defines common concepts for sub-ontologies of relevant domains and that is easily extensible towards existing and future ontologies. It is our intention to make this ontology available to other projects and collaboratively work on standardized ontology for the mobile domain.

Design, implementation and validation of a novel open framework for agile development of mobile health applications

The delivery of healthcare services has experienced tremendous changes during the last years. Mobile health or mHealth is a key engine of advance in the forefront of this revolution. Although there exists a growing development of mobile health applications, there is a lack of tools specifically devised for their implementation. This work presents mHealthDroid, an open source Android implementation of a mHealth Framework designed to facilitate the rapid and easy development of mHealth and biomedical apps. The framework is particularly planned to leverage the potential of mobile devices such as smartphones or tablets, wearable sensors and portable biomedical systems. These devices are increasingly used for the monitoring and delivery of personal health care and wellbeing. The framework implements several functionalities to support resource and communication abstraction, biomedical data acquisition, health knowledge extraction, persistent data storage, adaptive visualization, system management and value-added services such as intelligent alerts, recommendations and guidelines. An exemplary application is also presented along this work to demonstrate the potential of mHealthDroid. This app is used to investigate on the analysis of human behavior, which is considered to be one of the most prominent areas in mHealth. An accurate activity recognition model is developed and successfully validated in both offline and online conditions.

Modeling of sensor data and context for the Real World Internet

The Internet is expanding to reach the real world, integrating the physical world into the digital world in what is called the Real World Internet (RWI). Sensor and actuator networks deployed all over the Internet will play the role of collecting sensor data and context information from the physical world and integrating it into the future RWI. In this paper we present the SENSEI architecture approach for the RWI; a layered architecture composed of one or several context frameworks on top of a sensor framework, which allows the collection of sensor data as well as context information from the real world. We focus our discussion on how the modeling of information is done for different levels (sensor and context data), present a multi-layered information model, its representation and the mapping between its layers.

Bringing quality of context into wearable human activity recognition systems

Quality of Context (QoC) in context-aware computing improves reasoning and decision making. Activity recognition in wearable computing enables context-aware assistance. Wearable systems must include QoC to participate in context processing frameworks common in large ambient intelligence environments. However, QoC is not specifically defined in that domain. QoC models allowing activity recognition system reconfiguration to achieve a desired context quality are also missing. Here we identify the recognized dimensions of QoC and the performance metrics in activity recognition systems. We discuss how the latter maps on the former and provide provide guidelines to include QoC in activity recognition systems. On the basis of gesture recognition in a car manufacturing case study, we illustrate the signification of QoC and we present modeling abstractions to reconfigure an activity recognition system to achieve a desired QoC.

Multi-sensor Fusion Based on Asymmetric Decision Weighting for Robust Activity Recognition

The recognition of human activity has been deeply explored during the recent years. However, most proposed solutions are mainly devised to operate in ideal conditions, thus not addressing crucial real-world issues. One of the most prominent challenges refers to common sensor technological anomalies. Sensor faults and failures introduce variations in the measured sensor data with respect to the equivalent observations in ideal conditions. As a consequence, predefined recognition systems may potentially fail to identify actions in the anomalous sensor data. This paper presents a novel model devised to cope with the effects introduced by sensor technological anomalies. The model builds on the knowledge gained from multi-sensor configurations, through asymmetrically weighting the decisions provided at both activity and sensor levels. Insertion and rejection weighting metrics are particularly used to eventually yield a unique recognized activity. For the sake of comparison, the tolerance to sensor faults and failures of standard activity recognition systems and the new proposed model are evaluated. The results prove classic activity-aware systems to be incapable of recognition under the effects of sensor technological anomalies, while the proposed model demonstrates to be robust against both sensor faults and failures.

Context sessions: a novel approach for scalable context management in NGN networks

In this paper we present a context-management middleware for Next Generation Networks (NGN). Our middleware, called ICE, is based on the concept of Context Sessions. In contrast to earlier work in context management, we apply NGN design principles to context management and separate signalling from context exchange. We believe that ICE provides an ideal starting point for more flexible and adaptive management of large scale context information compared to existing approaches. In order to allow for easy integration into NGN networks, ICE is implemented as a service on top of the IP Multimedia Subsystem.

MIMU-Wear

An enormous effort has been made during the recent years towards the recognition of human activity based on wearable sensors. Despite the wide variety of proposed systems, most existing solutions have in common to solely operate on predefined settings and constrained sensor setups. Real-world activity recognition applications and users rather demand more flexible sensor configurations dealing with potential adverse situations such as defective or missing sensors. In order to provide interoperability and reconfigurability, heterogeneous sensors used in wearable activity recognition systems must be fairly abstracted from the actual underlying network infrastructure. This work presents MIMU-Wear, an extensible ontology that comprehensively describes wearable sensor platforms consisting of mainstream magnetic and inertial measurement units (MIMUs). MIMU-Wear describes the capabilities of MIMUs such as their measurement properties and the characteristics of wearable sensor platforms including their on-body location. A novel method to select an adequate replacement for a given anomalous or nonrecoverable sensor is also presented in this work. The proposed sensor selection method is based on the MIMU-Wear Ontology and builds on a set of heuristic rules to infer the candidate replacement sensors under different conditions. Then, queries are iteratively posed to select the most appropriate MIMU sensor for the replacement of the defective one. An exemplary application scenario is used to illustrate some of the potential of MIMU-Wear for supporting seamless operation of wearable activity recognition systems.

Human Behavior Analysis by Means of Multimodal Context Mining

There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several monitoring techniques have been proposed in the past to track users’ behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user’s context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels.