  Aging population ratios are rising significantly. Meanwhile, smart home based
health monitoring services are evolving rapidly to become a viable alternative
to traditional healthcare solutions. Such services can augment qualitative
analyses done by gerontologists with quantitative data. Hence, the recognition
of Activities of Daily Living (ADL) has become an active domain of research in
recent times. For a system to perform human activity recognition in a
real-world environment, multiple requirements exist, such as scalability,
robustness, ability to deal with uncertainty (e.g., missing sensor data), to
operate with multi-occupants and to take into account their privacy and
security. This paper attempts to address the requirements of scalability and
robustness, by describing a reasoning mechanism based on modular spatial and/or
temporal context models as a network of ontologies. The reasoning mechanism has
been implemented in a smart home system referred to as Arianna+. The paper
presents and discusses a use case, and experiments are performed on a simulated
dataset, to showcase Arianna+'s modularity feature, internal working, and
computational performance. Results indicate scalability and robustness for
human activity recognition processes.
