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commercially available sleep trackers to identify stages of sleep in test subject but aims
to use and explain the root causes of the quality of sleep using alternative data captured
from the environment where the test subject is resting.
4.4. Machine Learning Model Performance and Discussion
The assessment of each machine learning model is made by the execution of each
machine learning workflow. Each model assessment required a workflow as exemplified
in Figure 6. The output and prediction were stored in the same database where sensor
data was extracted. Scorer nodes may also be used to obtain direct model performance.
Figure 6. Knime MultiLayer Perceptron workflow
The results from the prediction of sleep phases are compiled in Table 4. These results
are obtained from the machine learning workflows defined in the workflow program
knime, which are trained, and stored as simplified and with models trained as PMML
machine learning model.
Later when a live system requires the analysis of sleep phases with data provided by
our mutlisensor architecture, the stored PMML model can be used to make prediction
based on the pre-trained models.
Table 4. Machine Learning models prediction of sleep stages
Algorithm Accuracy
(%) Error
(%) Accuracy
Awake (%) Accuracy
REM (%) Accuracy
Light (%) Accuracy
Deep (%)
Decision Tree 89 11 88 87 93 82
NaĂŻve Bayes 51 49 61 22 64 27
MLP 69 31 59 63 71 73
C.Gonçalvesetal. /MultisensorMonitoringSystem toEstablishCorrelations 33
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