The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing
Open accessAbstract
Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject's trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%.
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The WEKA data mining software: an update
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Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912
A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring
Yang, C.-C., & Hsu, Y.-L. (2010) · Sensors · doi:10.3390/s100807772 · Cited by 1,058
A Study on Human Activity Recognition Using Accelerometer Data from Smartphones
Bayat, A., Pomplun, M., & Tran, D. A. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.07.009 · Cited by 531
Cited by
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Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914030 · Cited by 594
Demrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020) · IEEE Access · doi:10.1109/access.2020.3037715 · Cited by 368
Fog Computing for Detecting Vehicular Congestion, an Internet of Vehicles Based Approach: A Review
Thakur, A., & Malekian, R. (2019) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2019.2903551 · Cited by 73
Self-citationWireless Capacitive-Based ECG Sensing for Feature Extraction and Mobile Health Monitoring
Wannenburg, J., Malekian, R., & Hancke, G. P. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2844122 · Cited by 66
Self-citationWieland, F., & Nigg, C. (2023) · JMIR AI · doi:10.2196/42337 · Cited by 8
Metadata
- Authors
- Wannenburg, J., & Malekian, R.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2017-12
- Volume
- 47
- Issue
- 12
- Pages
- 3142-3149
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2404247879
- Cited by
- 160
- References
- 4
- FWCI
- 14.83
- Type
- journal article
- Language
- en
- Open access
- green
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- Wannenburg, J., & Malekian, R. (2017). Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/tsmc.2016.2562509
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