  Smart home environments are designed to provide services that help improve
the quality of life for the occupant via a variety of sensors and actuators
installed throughout the space. Many automated actions taken by a smart home
are governed by the output of an underlying activity recognition system.
However, activity recognition systems may not be perfectly accurate and
therefore inconsistencies in smart home operations can lead users reliant on
smart home predictions to wonder "why did the smart home do that?" In this
work, we build on insights from Explainable Artificial Intelligence (XAI)
techniques and introduce an explainable activity recognition framework in which
we leverage leading XAI methods to generate natural language explanations that
explain what about an activity led to the given classification. Within the
context of remote caregiver monitoring, we perform a two-step evaluation: (a)
utilize ML experts to assess the sensibility of explanations, and (b) recruit
non-experts in two user remote caregiver monitoring scenarios, synchronous and
asynchronous, to assess the effectiveness of explanations generated via our
framework. Our results show that the XAI approach, SHAP, has a 92% success rate
in generating sensible explanations. Moreover, in 83% of sampled scenarios
users preferred natural language explanations over a simple activity label,
underscoring the need for explainable activity recognition systems. Finally, we
show that explanations generated by some XAI methods can lead users to lose
confidence in the accuracy of the underlying activity recognition model. We
make a recommendation regarding which existing XAI method leads to the best
performance in the domain of smart home automation, and discuss a range of
topics for future work to further improve explainable activity recognition.
