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. 2014 Feb 1;10(Pt B):138-154.
doi: 10.1016/j.pmcj.2012.07.003.

Activity Recognition on Streaming Sensor Data

Narayanan C Krishnan  1 , Diane J Cook  1
Affiliations

Activity Recognition on Streaming Sensor Data

Narayanan C Krishnan et al. Pervasive Mob Comput. .

Abstract

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.

Keywords: activity recognition; mutual information; online; real-time; streaming.

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Figures

Figure 1
Figure 1
Illustration of the different approaches for stream processing. The different motion/door sensor firings are depicted by the colored vertical lines. The color indicates the type and location of the sensor that was switched on. The sensor windows are obtained using a sliding window of length 10 sensor events.
Figure 2
Figure 2
An example of a sequence of sensor events from one of the smart home testbeds.
Figure 3
Figure 3
Illustration of the different dependencies when considering sensor based windows.
Figure 4
Figure 4
Effect of χ on the weights.
Figure 5
Figure 5
Illustration of the two phase learning process that includes past contextual information
Figure 6
Figure 6
The occurrences of the different activities in each of the smart apartment.
Figure 7
Figure 7
Mutual Information between every pair of sensors for the three smart apartment testbeds.
Figure 8
Figure 8
F scores for the individual activities for each testbed as obtained by the different approaches.
Figure 9
Figure 9
Normalized confusion matrix for each of the three testbeds. The labels are 1) Bathing, 2) Bed to Toilet, 3) Cook, 4) Eat, 5) Enter home, 6) Leave home, 7) Personal Hygiene, 8) Relax, 9) Sleep, 10) Take Medicine, 11) Work and 12) Other Activity.
See this image and copyright information in PMC

References

    1. Cook D, Schmitter-Edgecombe M, Crandall A, Sanders C, Thomas B. Collecting and disseminating smart home sensor data in the casas project. Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research. 2009
    1. Cook DJ, Youngblood M, Heierman E, Gopalratnam K, Rao S, Litvin A, Khawaja F. Mavhome: An agent-based smart home; Proceedings of the International Conference on Pervasive Computing; 2003.
    1. Intille S, Larson K, Tapia EM, Beaudin J, Kaushik P, Nawyn J, Rockinson R. Using a live-in laboratory for ubiquitous computing research. Proceedings of Pervasive. 2006:349–365.
    1. Krse BJA, Kasteren TLMV, Gibson CHS, den Dool TV. Care: Context awareness in residences for elderly; Proceedings of Sixth International Conference of the International Society for Gerontechnology; 2008.
    1. Kidd CD, Orr RJ, Abowd GD, Atkeson CG, Essa IA, Mac-Intyre B, Mynatt E, Starner TE, Newstetter W. The aware home: A living laboratory for ubiquitous computing research; Proceedings of the Second International Workshop on Cooperative Buildings; 1999.