The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors
Open accessAbstract
Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0:05, proving that the hybrid approach is better suited for the addressed domain.
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Advanced internet of things for personalised healthcare systems: A survey
Qi, J., Yang, P., Min, G., Amft, O., Dong, F., & Xu, L. (2017) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2017.06.018 · Cited by 406
Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches
Vaizman, Y., Ellis, K., & Lanckriet, G. (2017) · IEEE Pervasive Computing · doi:10.1109/mprv.2017.3971131 · Cited by 324
A systematic review of robotics research in support of in-home care for older adults
Abou Allaban, A., Wang, M., & Padır, T. (2020) · Information · doi:10.3390/info11020075 · Cited by 106
Measuring functional arm movement after stroke using a single wrist-worn sensor and machine learning
Bochniewicz, E. M., Emmer, G., McLeod, A., Barth, J., Dromerick, A. W., & Lum, P. (2017) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2017.07.004 · Cited by 73
Large Language Models are Zero-Shot Recognizers for Activities of Daily Living
Civitarese, G., Fiori, M., Choudhary, P., & Bettini, C. (2024) · arXiv · doi:10.48550/arxiv.2407.01238 · Cited by 28
Metadata
- Authors
- Ordóñez, F. J., De Toledo, P., & Sanchis, A.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2013-04-24
- Volume
- 13
- Issue
- 5
- Pages
- 5460-5477
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2034281151
- Cited by
- 277
- References
- 36
- FWCI
- 20.44
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ordóñez, F. J., De Toledo, P., & Sanchis, A. (2013). Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors. Sensors. https://doi.org/10.3390/s130505460
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