Activity recognition from smartphone data using weighted learning methods
| Autor: | Belkacem Fergani, M’hamed Bilal Abidine |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Activity recognition ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Learning methods 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Intelligenza Artificiale. 15:1-15 |
| ISSN: | 2211-0097 1724-8035 |
| Popis: | Mobile phone based activity recognition uses data obtained from embedded sensors to infer user’s physical activities. The traditional approach for activity recognition employs machine learning algorithms to learn from collected labeled data and induce a model. To enhance the accuracy and hence to improve the overall efficiency of the system, the good classifiers can be combined together. Fusion can be done at the feature level and also at the decision level. In this work, we propose a new hybrid classification model Weighted SVM-KNN to perform automatic recognition of activities that combines a Weighted Support Vector Machines (WSVM) to learn a model with a Weighted K-Nearest Neighbors (WKNN), to classify and identify the ongoing activity. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our method outperforms the state-of-the-art on a large benchmark datasets. |
| Databáze: | OpenAIRE |
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