  In the last few years there has been a growing interest in Human Activity
Recognition~(HAR) topic. Sensor-based HAR approaches, in particular, has been
gaining more popularity owing to their privacy preserving nature. Furthermore,
due to the widespread accessibility of the internet, a broad range of
streaming-based applications such as online HAR, has emerged over the past
decades. However, proposing sufficiently robust online activity recognition
approach in smart environment setting is still considered as a remarkable
challenge. This paper presents a novel online application of Hierarchical
Hidden Markov Model in order to detect the current activity on the live
streaming of sensor events. Our method consists of two phases. In the first
phase, data stream is segmented based on the beginning and ending of the
activity patterns. Also, on-going activity is reported with every receiving
observation. This phase is implemented using Hierarchical Hidden Markov models.
The second phase is devoted to the correction of the provided label for the
segmented data stream based on statistical features. The proposed model can
also discover the activities that happen during another activity - so-called
interrupted activities. After detecting the activity pane, the predicted label
will be corrected utilizing statistical features such as time of day at which
the activity happened and the duration of the activity. We validated our
proposed method by testing it against two different smart home datasets and
demonstrated its effectiveness, which is competing with the state-of-the-art
methods.
