Abstract
Activity recognition is fundamental to many applications envisaged in pervasive computing, especially in smart environments where the resident’s data collected from sensors will be mapped to human activities. Previous research usually focuses on scripted or pre-segmented sequences related to activities, whereas many real-world deployments require information about the ongoing activities in real time. In this paper, we propose an online activity recognition model on streaming sensor data that incorporates the spatio-temporal correlation-based dynamic segmentation method and the stigmergy-based emergent modeling method to recognize activities when new sensor events are recorded. The dynamic segmentation approach integrating sensor correlation and time correlation judges whether two consecutive sensor events belong to the same window or not, avoiding events from very different functional areas or with a long time interval in the same window, thus obtaining the segmented window for every single event. Then, the emergent paradigm with marker-based stigmergy is adopted to build activity features that are explicitly represented as a directed weighted network to define the context for the last sensor event in this window, which does not need sophisticated domain knowledge. We validate the proposed method utilizing the real-world dataset Aruba from the CASAS project and the results show the effectiveness.
Highlights
The great progress of ubiquitous computing has contributed to the rapid development of various sensors that are usually used to collect information of interest
We employ the five-fold cross-validation strategy and the offline phase is performed on the training dataset to compute sensor correlation matrix (SCM), sensor correlation threshold (SCT), maximum time interval (MTI) and maximum time span (MTS) according to Section 3.1
This paper presents an online activity recognition model on streaming sensor data for monitoring elderly behavior
Summary
The great progress of ubiquitous computing has contributed to the rapid development of various sensors that are usually used to collect information of interest. When combined with efficient machine learning or deep learning techniques, the collected information is very important for the development of a wide range of applications. One of the application areas is the smart home environment, in which human and environmental information is adopted to track the functional condition of interested objects. The aging population [1], the healthcare costs [2] and the desire for aging in place [3] highlight the necessity of developing these technologies. In order to live a functionally independent life, residents must have the ability to complete activities of daily living (ADLs), such as eating, bathing, etc. It is crucial to automatically recognize and track the ADLs of the interested objects for monitoring their functional status
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