Hierarchical classification method based on selective learning of slacked hierarchy for activity recognition systems |
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| Affiliation: | 1. Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand;2. Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand;3. Department of Microbiology, Faculty of Public Health, Mahidol University, Bangkok, Thailand;1. Advanced Visualization Laboratory–VizLab–Vale do Rio dos Sinos University (UNISINOS), Av. Unisinos, 950, São Leopoldo, 93022-000, RS, Brazil;2. Department of Civil Construction, Federal Institute of Santa Catarina (IFSC), Florianopolis, 88020-300, SC, Brazil;3. Institute of Geography, Federal University of Uberlandia (UFU), Monte Carmelo, 38500-000, MG, Brazil;4. Graduate Program in Transportation Engineering, University of São Paulo, São Carlos School of Engineering (EESC), São Carlos - SP, Brazil;1. Luxembourg Institute of Socio-Economic Research (LISER), Maison des Sciences Humaines, 11, Porte des Sciences L- 4366 Esch-sur-Alzette, Luxembourg\n;2. University of Salerno, Via Giovanni Paolo II, 132 84084 Fisciano (SA), Italy;1. Department of Electronics and Communication Engineering, National Institute of Technology Goa, Farmagudi, Ponda, Goa, 403401, India;2. Department of Instrumentation and Control Engineering, PSG College of Technology, Coimbatore, 641004, India;3. Department of Electronics and Communication Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, 500 043, India;1. CINI Assistive Technologies National Lab & DAUIN, Politecnico di Torino, Italy;2. Department of Mathematics, University of Turin, Via Carlo Alberto 10, 10121 Torino, Italy;3. Istituto Superiore Mario Boella, Center for Applied Research on ICT, Via Pier Carlo Boggio 61, 10138, Torino, Italy |
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| Abstract: | Physical activity recognition using wearable sensors has gained significant interest from researchers working in the field of ambient intelligence and human behavior analysis. The problem of multi-class classification is an important issue in the applications which naturally has more than two classes. A well-known strategy to convert a multi-class classification problem into binary sub-problems is the error-correcting output coding (ECOC) method. Since existing methods use a single classifier with ECOC without considering the dependency among multiple classifiers, it often fails to generalize the performance and parameters in a real-life application, where different numbers of devices, sensors and sampling rates are used. To address this problem, we propose a unique hierarchical classification model based on the combination of two base binary classifiers using selective learning of slacked hierarchy and integrating the training of binary classifiers into a unified objective function. Our method maps the multi-class classification problem to multi-level classification. A multi-tier voting scheme has been introduced to provide a final classification label at each level of the solicited model. The proposed method is evaluated on two publicly available datasets and compared with independent base classifiers. Furthermore, it has also been tested on real-life sensor readings for 3 different subjects to recognize four activities i.e. Walking, Standing, Jogging and Sitting. The presented method uses same hierarchical levels and parameters to achieve better performance on all three datasets having different number of devices, sensors and sampling rates. The average accuracies on publicly available dataset and real-life sensor readings were recorded to be 95% and 85%, respectively. The experimental results validate the effectiveness and generality of the proposed method in terms of performance and parameters. |
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