Behaviour Profiling in Healthcare Applications Using the Internet of Things Technology

This position paper advocates applying the monitoring pogwer of IoT to build profiles of user behaviour using the large volumes of collected data. The desired system exploits sensor data mining approaches to profile user behaviour patterns in smart environments. Sensor data is mined to extract relationships of interest between environmental variables (context) and the user, building in this way behaviour profiles. The capability of applying knowledge to manipulate user’s environment is expected take monitoring beyond the simple alert-mode of operation to long term profiling of user’s behaviour. After a brief literature review to prove the suitability of IoT as a low-cost unsupervised profiling platform, we give the details of our proposal and the objectives that needs to be met before user behaviour profiling across inter-spaces is possible

Textile sensors to measure sweat pH and sweat-rate during exercise

Sweat analysis can provide a valuable insight into a person’s well-being. Here we present wearable textile-based sensors that can provide real-time information regarding sweat activity. A pH sensitive dye incorporated into a fabric fluidic system is used to determine sweat pH. To detect the onset of sweat activity a sweat rate sensor is incorporated into a textile substrate. The sensors are integrated into a waistband and controlled by a central unit with wireless connectivity. The use of such sensors for sweat analysis may provide valuable physiological information for applications in sports performance and also in healthcare

SPINE: A domain-specific framework for rapid prototyping of WBSN applications

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Von persönlicher Schutzbekleidung zum mobilen Schutzassistenzsystem

Miniaturized and embedded computers open new prospects for Personal Protective Equipment (PPE). PPE will recognize context and react on environmental hazards in an autonomous way in the future. Networked components may predict dangerous situations. These complex systems demand a new participatory design process because the new protective functions have to adjust between user and automated technique for practical use. This PhD thesis deals with the user-oriented development process for these new ambient assisted protection systems. A specific workflow follows the process-oriented and networked character of the new mobile protection system. In addition designated design attributes motivate the need of clothing related solutions

Information integration platform for patient-centric healthcare services: design, prototype and dependability aspects

Published version of an article in the journal: Future Internet. Also available from the publisher at: http://dx.doi.org/10.3390/fi6010126 Open AccessTechnology innovations have pushed today’s healthcare sector to an unprecedented new level. Various portable and wearable medical and fitness devices are being sold in the consumer market to provide the self-empowerment of a healthier lifestyle to society. Many vendors provide additional cloud-based services for devices they manufacture, enabling the users to visualize, store and share the gathered information through the Internet. However, most of these services are integrated with the devices in a closed “silo” manner, where the devices can only be used with the provided services. To tackle this issue, an information integration platform (IIP) has been developed to support communications between devices and Internet-based services in an event-driven fashion by adopting service-oriented architecture (SOA) principles and a publish/subscribe messaging pattern. It follows the “Internet of Things” (IoT) idea of connecting everyday objects to various networks and to enable the dissemination of the gathered information to the global information space through the Internet. A patient-centric healthcare service environment is chosen as the target scenario for the deployment of the platform, as this is a domain where IoT can have a direct positive impact on quality of life enhancement. This paper describes the developed platform, with emphasis on dependability aspects, including availability, scalability and security

Energy-efficient Continuous Context Sensing on Mobile Phones

With the ever increasing adoption of smartphones worldwide, researchers have found the perfect sensor platform to perform context-based research and to prepare for context-based services to be also deployed for the end-users. However, continuous context sensing imposes a considerable challenge in balancing the energy consumption of the sensors, the accuracy of the recognized context and its latency. After outlining the common characteristics of continuous sensing systems, we present a detailed overview of the state of the art, from sensors sub-systems to context inference algorithms. Then, we present the three main contribution of this thesis. The first approach we present is based on the use of local communications to exchange sensing information with neighboring devices. As proximity, location and environmental information can be obtained from nearby smartphones, we design a protocol for synchronizing the exchanges and fairly distribute the sensing tasks. We show both theoretically and experimentally the reduction in energy needed when the devices can collaborate. The second approach focuses on the way to schedule mobile sensors, optimizing for both the accuracy and energy needs. We formulate the optimal sensing problem as a decision problem and propose a two-tier framework for approximating its solution. The first tier is responsible for segmenting the sensor measurement time series, by fitting various models. The second tier takes care of estimating the optimal sampling, selecting the measurements that contributes the most to the model accuracy. We provide near-optimal heuristics for both tiers and evaluate their performances using environmental sensor data. In the third approach we propose an online algorithm that identifies repeated patterns in time series and produces a compressed symbolic stream. The first symbolic transformation is based on clustering with the raw sensor data. Whereas the next iterations encode repetitive sequences of symbols into new symbols. We define also a metric to evaluate the symbolization methods with regard to their capacity at preserving the systems' states. We also show that the output of symbols can be used directly for various data mining tasks, such as classification or forecasting, without impacting much the accuracy, but greatly reducing the complexity and running time. In addition, we also present an example of application, assessing the user's exposure to air pollutants, which demonstrates the many opportunities to enhance contextual information when fusing sensor data from different sources. On one side we gather fine grained air quality information from mobile sensor deployments and aggregate them with an interpolation model. And, on the other side, we continuously capture the user's context, including location, activity and surrounding air quality. We also present the various models used for fusing all these information in order to produce the exposure estimation