Information Technology Reference
In-Depth Information
Chapter 1
Introduction
1.1 Motivation
In the last years, recent advances in computing and sensing technologies have
contributed with novel ideas aiming to solve people's needs in a wide range of situa-
tions: from basic daily living essentials such as personal care, feeding and mobility,
to more complex social issues including health, education and security. Automated
systems are an example of the materialization of these ideas. They exploit informa-
tion gathered from users and their environment in order to produce an appropriate
action (Campbell et al.
2008
). In this thesis, we are interested in exploring the devel-
opment of systems that promote the improvement of people's Quality of Life (QoL)
through the
recognition of human activities
, especially, that of individuals with any
type of limitation (e.g. the disabled and the elderly) and lack of general well-being.
Understanding people's actions and their interaction with the environment is a
key element for the development of the aforementioned intelligent systems. HAR
is a research field that specifically deals with this issue through the integration of
sensing and reasoning, in order to deliver context-aware data that can be employed
to provide personalized support in many applications (Chen et al.
2012
). As a simple
example, imagine a
smart home
equipped with ambient sensors able to detect peo-
ple's presence and the activation of household appliances. It is possible to infer the
activities performed by its residents based on the sensors signals along with other
relevant aspects such as time of the day and date (e.g. a person in the kitchen during
morning time while a coffee machine is on suggests that person is making break-
fast). Consequently, the collected HAR information can be exploited to anticipate
future people requirements and become responsive to them (e.g. by automatically
pre-heating the coffee machine, controlling room lighting and temperature, etc.).
In the HAR framework, there are still several issues that need to be addressed,
some of which are: obtrusiveness of current wearable sensors; lack of fully perva-
sive systems able to reach users at any location any time; privacy concerns regard-
ing invasive and continuous monitoring of activities (e.g. by using video cameras);
difficulty of performing HAR in real-time; battery limitations of wearable devices;
and dealing with content extraction from sparse multisensor data.
