Activity Recognition on Streaming Sensor Data

By Narayanan Chatapuram Krishnan, Diane Joyce Cook. Pervasive and Mobile Computing, 2012

518 citations recorded by the source corpus.

Many real-world applications that focus on addressing needs of a human, require information about the activities being performed by the human in real-time. While advances in pervasive computing have lead to the development of wireless and non-intrusive sensors that can capture the necessary activity information, current activity recognition approaches have so far experimented on either a scripted or pre-segmented sequence of sensor events related to activities. In this paper we propose and evaluate a sliding window based approach to perform activity recognition in an on line or streaming fashion; recognizing activities as and when new sensor events are recorded. To account for the fact that different activities can be best characterized by different window lengths of sensor events, we incorporate the time decay and mutual information based weighting of sensor events within a window. Additional contextual information in the form of the previous activity and the activity of the previous window is also appended to the feature describing a sensor window. The experiments conducted to evaluate these techniques on real-world smart home datasets suggests that combining mutual information based weighting of sensor events and adding past contextual information into the feature leads to best performance for streaming activity recognition.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/fc79c74063d5183cfbc0994be7b2dcab26bed937

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In Computer Networks and Communications

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Citations in the corpus

An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments (2017, methodology): “Moreover, recognizing user activities such as eating, cooking, sleeping, or working [7], [44], is one of the major challenges of AmI systems.”
Adversarial Transfer Learning (2018, background): “For instance, the activity recognition models that have been developed for smart homes [65] could potentially be adapted to operate on data from another set of sensors, e.”
Combining emerging patterns with random forest for complex activity recognition in smart homes (2018, background): “One of the possible solutions is to exploit dynamic segmentation methods [3].”
A Survey of Unsupervised Deep Domain Adaptation (2018, unknown): “ sensors (e.g. RFID), it may be beneficial to adapt the model to be used on other sensors (e.g. motion sensors). For instance, the activity recognition models that have been developed for smart homes [65] could potentially be adapted to operate on data from another set of sensors, e.g. a different s”