Automatically detecting activities of daily living from in-home sensors
  as indicators of routine behaviour in an older population
Claire M. Timon Pamela Hussey Hyowon Lee Catriona Murphy Harsh Vardan Rai and Alan F. Smeaton
Objective: The NEX project has developed an integrated Internet of Things
(IoT) system coupled with data analytics to offer unobtrusive health and
wellness monitoring supporting older adults living independently at home.
Monitoring {currently} involves visualising a set of automatically detected
activities of daily living (ADLs) for each participant. The detection of ADLs
is achieved {} to allow the incorporation of additional participants whose ADLs
are detected without re-training the system.
  Methods: Following an extensive User Needs and Requirements study involving
426 participants, a pilot trial and a friendly trial of the deployment, an
Action Research Cycle (ARC) trial was completed. This involved 23 participants
over a 10-week period each with c.20 IoT sensors in their homes. During the ARC
trial, participants each took part in two data-informed briefings which
presented visualisations of their own in-home activities. The briefings also
gathered training data on the accuracy of detected activities. Association rule
mining was then used on the combination of data from sensors and participant
feedback to improve the automatic detection of ADLs.
  Results: Association rule mining was used to detect a range of ADLs for each
participant independently of others and was then used to detect ADLs across
participants using a single set of rules {for each ADL}. This allows additional
participants to be added without the necessity of them providing training data.
  Conclusions: Additional participants can be added to the NEX system without
the necessity to re-train the system for automatic detection of the set of
their activities of daily living.