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was also considered. The timestamp values of each entry of the dataset were converted
into two integer values, representing the hour and the minute of the corresponding
timestamp. In addition, some other data were added to each dataset entry, such as the
number of the experiment or session (1 to 7), the starting timestamp of the experiment,
also converted into two integer values, total sleep time and the time spent in each sleep
stage (Awake, REM, Light and Deep) for the corresponding experiment (in minutes).
Finally, to conclude the preparation of the dataset, and considering the information
obtained from the Fitbit wristband app, each entry of the dataset received the total
number of steps taken by the subject during the corresponding day and, for the respective
timestamps, the sleep stage estimation, converted to a numerical value: 0 for awake stage,
1 for REM, 2 for Light and 3 for Deep.
Figure 5. Fitbit sleep stages estimation for the first night of the experiment.
4.3. Machine Learning Model Development
Initial experiments are a means to demonstrate the validity of the model and the
prototypes being developed. In this case, the data from 7 experiments was considered
and used to develop machine learning models to estimate sleep phase from historic time
series data. The data used, came from the environment multisensory, bedside
accelerometer while sleep identification came from the Fitbit wearable. As presented in
Figure 2, the data was stored in local database which is accessible by machine learning
workflows developed in the Knime platform [15].
The machine learning workflow was developed using machine learning algorithms
and is compatible with the PMML specification [14] which means it can be transported
to other machine learning frameworks and retain the same characteristics. With this
approach the research team is able to test different theories and allows the rapid
development of decision support systems.
The data was prepared following CRISP-DM methodology where, the data domain
was studied as detailed in section 1 and 3, the data was prepared for each algorithm which
implied the transformation of simple data rows into summaries of one minute for each
sensors and then a moving average of the last 15 and 30 minutes. This implies that the
decision support system requires at least 30 min input data before it can predict sleep
phase during sleep sessions. In the model evaluation stage, specific data preparation was
required due to algorithmic restrictions.
The algorithms being tested were, NaĂŻve Bayes, Multilayer Perceptron and C4.5
based decision tree. These experiments lay the foundation to study the effects on
environments on the quality of sleep. The methodologies employed takes advantage of
C.Gonçalvesetal. /MultisensorMonitoringSystem
toEstablishCorrelations32
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