An Event-Based Approach for Discovering Activities of Daily Living by Hidden Markov Models
| Autor: | Jean-Jacques Lesage, Kevin Viard, Maria Pia Fanti, Gregory Faraut |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Activities of daily living Computer science business.industry Event based media_common.quotation_subject 02 engineering and technology Machine learning computer.software_genre 020901 industrial engineering & automation Home automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Artificial intelligence Hidden Markov model business computer media_common |
| Zdroj: | IUCC-CSS |
| DOI: | 10.1109/iucc-css.2016.020 |
| Popis: | Smart Home technologies may improve the comfort and the safety of frail people into their home. To achieve this goal, models of Activities of Daily Living (ADL) are often used to detect dangerous situations or behavioral changes in the habits of these persons. In this paper, an approach is proposed to build a model of ADLs, under the form of Hidden Markov Models (HMMs), from a training database of observed events emitted by binary sensors. The main advantage of our approach is that no knowledge of actions really performed during the learning period is required. Finally, we apply our approach to a real case study and we discuss the quality of the results obtained. |
| Databáze: | OpenAIRE |
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