Using respiratory signals for the recognition of human activities
| Autor: | Raul I. Ramos-Garcia, Edward Sazonov, Stephen T. Tiffany |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Adult
Male Engineering Speech recognition 0206 medical engineering Normal Distribution Wearable computer Walking 02 engineering and technology Secondary source Activity recognition Eating Inertial measurement unit 0202 electrical engineering electronic engineering information engineering Humans Human Activities Hidden Markov model Monitoring Physiologic Respiratory Sounds business.industry Smoking Frame (networking) Signal Processing Computer-Assisted 020206 networking & telecommunications Mixture model 020601 biomedical engineering Markov Chains Breathing Female business |
| Zdroj: | EMBC |
| DOI: | 10.1109/embc.2016.7590668 |
| Popis: | Human activity recognition through wearable sensors is becoming integral to health monitoring and other applications. Typically, human activity is captured through signals from inertial sensors, while signals from other sensors have been utilized less frequently. In this study, we explored the feasibility of classifying human activities by analyzing the temporal information of respiratory signals through hidden Markov models (HMMs). Left-to-right HMMs were trained for five activities: sedentary, walking, eating, talking, and cigarette smoking. The temporal information from every breathing segment was captured by fragmenting the tidal volume and airflow signals into smaller frames and computing features for each frame. These frames were used as observations to model the states of the HMMs through mixture of Gaussians. Using leave-one-out cross-validation, the classification performance showed an average precision, recall, and F-score of 60.37%, 67.01%, and 62.78%, respectively. Results suggest that respiratory signals can potentially be used as a primary or secondary source in the recognition of some human activities. |
| Databáze: | OpenAIRE |
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