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The analysis of results demonstrates that in our approach, the decision tree algorithm
performed better, not only at global accuracy but also individual sleep phase detection.
The multilayer perceptron could be improved by experimenting with different hidden
layers and more iteration per row, however, they lack the ability to give clues about
which features are more important from environment conditions, so they were
disregarded.
Focusing on the analysis of the decision tree, it becomes clear that moving average
input are determinant as they appear in the upper nodes of the decision tree along with
accelerometer data. Surprisingly, gas sensor data from the MQ6 and MQ135(CO2)
sensors are the first to appear in the upper nodes of the decision tree. These observations
motivate further studies to access the influence of such gases on sleep patterns.
Aside from being able to identify sleep stages by time series data, this approach is
able to characterize how active a person is by estimating the number of steps a person
made analyzing time series sleep data (Figure 7). Using the same approach, with the
number of steps extracted from the Fitbit wearable and with sleep data as input in moving
average windows, it was possible to estimate daily number of steps with as mean absolute
error of 87 steps and a mean squared error of 288. This provides evidence that is possible
to estimate the activity of people by analyzing only sleep data of about 30 min with our
multisensor architecture and sleep classification from the Fitbit wearable. The next stage
is to remove the need for classified sleep patterns, which came from the Fitbit wearable,
making this solution to use only accelerometer data and environmental data.
Figure 7. Machine Learning workflow for step estimation from sleep data
5. Conclusions and Future Work
The study here presented infers quality of sleep tracking using environmental and
accelerometer data from people. The results demonstrate that it is possible with a good
C.Gonçalvesetal. /MultisensorMonitoringSystem
toEstablishCorrelations34
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