Information gain-based metric for recognizing transitions in human activities
| Autor: | Flora D. Salim, Yongli Ren, Amin Sadri |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer Networks and Communications
Computer science Unsupervised segmentation 020206 networking & telecommunications 02 engineering and technology computer.software_genre Computer Science Applications Activity recognition Hardware and Architecture Robustness (computer science) 020204 information systems 0202 electrical engineering electronic engineering information engineering Segmentation Granularity Data mining Information gain computer Software Information Systems |
| Zdroj: | Pervasive and Mobile Computing. 38:92-109 |
| ISSN: | 1574-1192 |
| DOI: | 10.1016/j.pmcj.2017.01.003 |
| Popis: | This paper aims to observe and recognize transition times, when human activities change. No generic method has been proposed for extracting transition times at different levels of activity granularity. Existing work in human behavior analysis and activity recognition has mainly used predefined sliding windows or fixed segments, either at low-level, such as standing or walking, or high-level, such as dining or commuting to work. We present an Information Gain-based Temporal Segmentation method (IGTS), an unsupervised segmentation technique, to find the transition times in human activities and daily routines, from heterogeneous sensor data. The proposed IGTS method is applicable for low-level activities, where each segment captures a single activity, such as walking, that is going to be recognized or predicted, and also for high-level activities. The heterogeneity of sensor data is dealt with a data transformation stage. The generic method has been thoroughly evaluated on a variety of labeled and unlabeled activity recognition and routine datasets from smartphones and device-free infrastructures. The experiment results demonstrate the robustness of the method, as all segments of low- and high-level activities can be captured from different datasets with minimum error and high computational efficiency. |
| Databáze: | OpenAIRE |
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