User care at home is a matter of great concern since unforeseen
circumstances might occur that affect people's well-being. Technologies
that assist people in independent living are essential for enhancing
care in a cost-effective and reliable manner. Assisted care applications
often demand real-time observation of the environment and the resident's
activities using an event-driven system. As an emerging area of research
and development, it is necessary to explore the approaches of the user
care system in the literature to identify current practices for future
research directions.
Therefore, this book is aimed at a comprehensive review of data sources
(e.g., sensors) with machine learning for various smart user care
systems. To encourage the readers in the field, insights of practical
essence of different machine learning algorithms with sensor data (e.g.,
publicly available datasets) are also discussed. Some code segments are
also included to motivate the researchers of the related fields to
practically implement the features and machine learning techniques. It
is an effort to obtain knowledge of different types of sensor-based user
monitoring technologies in-home environments. With the aim of adopting
these technologies, research works, and their outcomes are reported.
Besides, up to date references are included for the user monitoring
technologies with the aim of facilitating independent living.
Research that is related to the use of user monitoring technologies in
assisted living is very widespread, but it is still consists mostly of
limited-scale studies. Hence, user monitoring technology is a very
promising field, especially for long-term care. However, monitoring of
the users for smart assisted technologies should be taken to the next
level with more detailed studies that evaluate and demonstrate their
potential to contribute to prolonging the independent living of people.
The target of this book is to contribute towards that direction.
