Activity recognition on streaming sensor data
Open accessAbstract
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.
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Naïve Bayes classifiers often work much better than expected, especially when a specific group of sensors can easily be identified as characteristic of a certain activity.
“In multiple cases, in spite of its simple design and simplified assumptions, naïve Bayes classifiers often work much better than expected, especially when a specific group of sensors can easily be identified as characteristic of a certain activity [9].”
From Sensor-Based Early Activity Recognition Inside Buildings to Support Energy and Comfort Management Systems (2019) · cites this paper as [9]
Section 2.2 (page not provided) says simple naïve Bayes classifiers have yielded promising results for offline activity recognition when ample training data is available. Section 5.2 also says the authors had used naïve Bayes successfully, but neither passage says it performs better than expected or links its performance to identifying a group of sensors characteristic of an activity. (LLM, conf=0.82)
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- Authors
- Krishnan, N. C., & Cook, D. J.
- Affiliations
- Washington State University (US)
- Corresponding author
- Krishnan, Narayanan C.
- Venue
- Pervasive and Mobile Computing
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 10
- Issue
- Pt B
- Pages
- 138-154
- ISSN
- 1574-1192 (ISSN-L)
- PMC
- PMC3979570
- OpenAlex
- W2135090840
- Cited by
- 488
- References
- 43
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 17 · 2023: 16 · 2022: 31 · 2021: 37 · 2020: 36 · 2019: 55 · 2018: 70 · 2017: 72 · 2016: 61 · 2015: 48 · 2014: 17 · 2013: 11
- FWCI
- 25.04
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Pervasive and Mobile Computing (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Biomedical Imaging and Bioengineering (R01EB009675, R01EB015853)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- activity recognition, streaming sensor data, sliding window, online recognition, contextual features, home automation, non-intrusive sensors, ubiquitous computing
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