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.
Robust activity recognition in near real-time is a prerequisite for delivering the smartness intrinsic to the pragmatic realisation of smart homes, environments and so forth. Many of the physical devices necessary for equipping a smart home are already available as consumer electronic devices and certified for use by the public. Yet activity recognition remains the preserve of the research community, despite the array of machine learning and other AI techniques currently available. To-date, research has been dominated by the use of pre-segmented data, resulting in the recognition of an arbitrary activity subsequent to its completion. For assistive paradigms dependent on smart technologies, for example Ambient Assisted Living, such approaches are insufficient. The overall objective must be the identification of an activity within an appropriative confidence level as soon as possible after activity commencement. This paper presents a novel approach, COBRA (Cumulatively Overlapping windowing approach for AmBient Recognition of Activities), for near real-time activity recognition, specifically within 60 seconds of the commencement of an activity. COBRA utilises an innovative combination of sliding windows augmented with a logistic regression model. The approach is evaluated using the well-established, open, CASAS dataset.