Turkish Journal of Electrical Engineering and Computer Sciences




Human activity recognition (HAR) is a critical process for applications that focus on the classification of human physical activities such as jogging, walking, downstairs, and upstairs. Ordinal classification (OC) is a special type of supervised multi-class classification in which an inherent ordering among the classes exists, such as low, medium, and high. This study combines these two concepts and introduces an approach to ?human activity recognition based on ordinal classification? (HAROC). In the proposed approach, ordinal classification is applied to human activity recognition where the physical activities can be ordered by using their signals? band power values. This is the first study that investigates the performance of the HAROC approach by combining the ordinal classification with eight different base learners. Besides, this study is also original in that it examines the effects of the demographic characteristics of the participants (i.e., sex, age, weight, and height) on the classification performance. The experiments carried out on a real-world dataset show that the proposed HAROC approach is an effective method for human activity recognition tasks.


Human activity recognition, ordinal classification, machine learning

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