Supporting context-aware engineering based on stream reasoning

In a world of increasing dynamism, context-awareness gives promise through the ability to detect changes in the context of devices, environment, and people. Equally, with stream reasoning using languages including C-SPARQL, continuous streams of raw data in RDF can be reasoned over for context awareness. Writing many context queries and rules this way can however be error prone, and often contains boilerplate. In this paper, we present a context modelling notation designed to support the creation of context-awareness based on stream reasoning systems. In validating our language there is tool support which, amongst other benefits, can generate context queries in C-SPARQL and context aggregation rules for higher level context knowledge processing. An Android compatible mobile platform context reasoner was developed which can handle these deployable context rules. This methodology and associated tools has been validated as part of an EU funded project

Managing multi-user smart environments through BLE based system

Smart and intelligent environment systems will be increasingly important in everyday life. Designing and developing them requires to face some research problems. A properly management of different types of sensors is very challenging but necessary in this area. A new wave of smart devices and systems brings customizable benefits for different users. Example of this are smart-phones, smartbands, smart-watches, smart homes or smart cars. A successful combination of them bring interesting everyday life benefits. One of the most important problems that needs to be solved in a practical way is the so called "multi-users problem". Addressing it is fundamental for moving forward into a successful adoption in everyday life of the smart technologies mentioned before. Associating users and services in a spaces in which there are many possible combination of them, is a core task. This work proposes a novel system based on interactions between Android smart-phones and Bluetooth Low Energy (BLE) technology beacons to deal with the challenges of a multi-user smart environment. We process data collected in a smart environment populated by many users. In particular we associate data collected and beacons. This processing generates database log traces containing measurements related to single user activities, helping in better matching services with users

Effect of modified-release methylphenidate on cognition in children with ADHD: evidence from a temporal preparation task

ADHD is associated with various cognitive deficits, including general performance decrements and specific impairments, for instance in temporal processing. However, time preparation under uncertain conditions has been under-investigated in this population. We aimed at filling this gap. We administered a variable foreperiod paradigm to children with ADHD before and after a one-month treatment with modified-release methylphenidate. Age-matched ADHD children with no treatment and healthy children were also tested as control groups with the same time-schedule. Children with ADHD had general performance deficits (longer and more variable response times), which disappeared in the experimental group after pharmacological intervention. Moreover, ADHD children showed a marked dependency on sequential foreperiod effects (i.e., slower responses for longer preceding foreperiods), especially at short current foreperiods, which were not modulated by the pharmacological treatment. In conclusion, the present findings show that methylphenidate enhances general motor processes rather than more specific time preparation processes, some of which appear deviant in ADHD

Improving the adaptation process for a new smart home user

Artificial Intelligence (AI) has been around for many years and plays a vital role in developing automatic systems that require decision using a data- or model-driven approach. Smart homes are one such system; in them, AI is used to recognize user activities, which is a fundamental task in smart home system design.There are many approaches to this challenge, but data-driven activity recognition approaches are currently perceived the most promising to address the sensor selection uncertainty problem. However, a smart home using a data-driven approach exclusively cannot immediately provide its new occupant with the expected functionality, which has reduced the popularity of the datadriven approach. This paper proposes an approach to develop an integrated personalized system using a user-centric approach comprising survey, simulation, activity recognition and transfer learning. This system will optimize the behaviour of the house using information from the user’s experience and provide required services. The proposed approach has been implemented in a smart home and validated with actual users. The validation results indicate that users benefited from smart features as soon as they move into the new hom

Developing navigational services for people with Down's Syndrome

The ability to commute and travel alone is an important skill that enables people to be more independent, and integrated with society. People with Down's Syndrome often experience low social integration, and low degree of independence. As part of the European Commission funded POSEIDON project, we want to explore how context-aware, and assistive technology can enable users with Down's Syndrome be more independent, including the ability to commute alone to a place of interest. In this paper, we report on our current progress in developing navigational services within the context of the POSEIDON project. We carried out a semi-structured qualitative evaluation of an early version of our navigational services with 6 individuals with Down's Syndrome, and report on our findings

Context-awareness to increase inclusion of people with DS in society

Assistive technologies have the potential to enhance the quality of life of citizens. Most especially of interest are those cases where a person is affected by some physical or cognitive impairment. Whilst most work in this area have been focused on assisting people indoors to support their independence, the POSEIDON project is focused on empowering citizens with Down’s Syndrome to support their independence outdoors. This paper explains the POSEIDON module which we are in the process of developing to make the system context-aware,reactive and adaptive

A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation

Recent smart home applications enhance the quality of people's home experiences by detecting their daily activities and providing them services that make their daily life more comfortable and safe. Human activity recognition is one of the fundamental tasks that a smart home should accomplish. However, there are still several challenges for such recognition in smart homes, with the target home adaptation process being one of the most critical, since new home environments do not have sufficient data to initiate the necessary activity recognition process. The transfer learning approach is considered the solution to this challenge, due to its ability to improve the adaptation process. This paper endeavours to provide a concrete review of user-centred smart homes along with the recent advancements in transfer learning for activity recognition. Furthermore, the paper proposes an integrated, personalised system that is able to create a dataset for target homes using both survey and transfer learning approaches, providing a personalised dataset based on user preferences and feedback