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accuracy to predict sleep stages with time series machine learning models, where data
has been transformed into data windows. The advantages of these techniques are that not
only can sleep tracking be made with accelerometer and environment sensor but we can
also estimate how active the person was during the day using trained models and 30 min
sample data.
In future work the team will continue this study and include different test subjects
sleeping in the same environment to adjust machine learning models to different people
in the same environment. The team is also interested in predicting the activity of test
subjects from a daily routine such as sports and sedentarism from the analysis of our
multisensory system.
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C.Gonçalvesetal. /MultisensorMonitoringSystem toEstablishCorrelations 35
Intelligent Environments 2019
Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Title
- Intelligent Environments 2019
- Subtitle
- Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Authors
- Andrés Muñoz
- Sofia Ouhbi
- Wolfgang Minker
- Loubna Echabbi
- Miguel Navarro-CĂa
- Publisher
- IOS Press BV
- Date
- 2019
- Language
- German
- License
- CC BY-NC 4.0
- ISBN
- 978-1-61499-983-6
- Size
- 16.0 x 24.0 cm
- Pages
- 416
- Category
- Tagungsbände