Improving activity recognition using temporal coherence
| Autor: | Maeva Doron, Pascal Bianchi, Abbas Ataya, Pierre Jallon |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Adult
Male Time Factors Computer science Movement Feature extraction Acceleration Wearable computer Markov process Monitoring Ambulatory Machine learning computer.software_genre Activity recognition symbols.namesake Young Adult Artificial Intelligence Activities of Daily Living Feature (machine learning) Humans Markov chain business.industry Reproducibility of Results Pattern recognition Signal Processing Computer-Assisted Directed graph Middle Aged Markov Chains symbols Regression Analysis Female Artificial intelligence business computer Classifier (UML) Algorithms |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | Assessment of daily physical activity using data from wearable sensors has recently become a prominent research area in the biomedical engineering field and a substantial application for pattern recognition. In this paper, we present an accelerometer-based activity recognition scheme on the basis of a hierarchical structured classifier. A first step consists of distinguishing static activities from dynamic ones in order to extract relevant features for each activity type. Next, a separate classifier is applied to detect more specific activities of the same type. On top of our activity recognition system, we introduce a novel approach to take into account the temporal coherence of activities. Inter-activity transition information is modeled by a directed graph Markov chain. Confidence measures in activity classes are then evaluated from conventional classifier's outputs and coupled with the graph to reinforce activity estimation. Accurate results and significant improvement of activity detection are obtained when applying our system for the recognition of 9 activities for 48 subjects. |
| Databáze: | OpenAIRE |
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