Determining the optimal number of sensors and optimal sensor layout

In order to find out which number of sensors would be optimal for our application, the simulation framework can be used to easily equip the virtual prototype with simulated sensors. Hence, the sim-ulated bed cover has been equipped with different numbers of sensors, ranging from 4 to 676. We distributed the sensors homogeneously in order to cover the full surface of the bed cover. The simu-lated sensor data has been recorded by letting the bed cover with different numbers of sensors fall on the 3D shape of the 10 test persons. The same evaluation process, mean calculation of F-measure of the leave one subject out cross-validation using SVM, results in the mean F-measures depicted in Figure 4.9.

4 16 25 40 64 196 676

20 40 60 80 100

Virtual prototype simulation with different numbers of sensors

Number of sensors

F−measure (%)

Figure 4.9.: Mean F-measure of total coverage layout with increasing number of sensors. Using the simulation framework up to 676 sensors are simulated on the prototype. The F-measure versus number of sensors trade-off is reached using 40 sensors.

We observe that in the interval of 4 to 40 sensors the highest F-measure gain is achieved, with its highest value at 40 sensors. Further increasing the sensor count to 64, 196 and 676 does not significantly

4. Simulation framework for designing flexible Smart Environment applications

improve the results. Possible reasons might be that more sensors do not offer additional information that improves the feature selection. This is caused by the redundancy of information or because of over-fitting produced by sensors detecting small wrinkles in the bed cover. Hence, the simulation results confirm the trade-off between sensor count and improved F-measure as well as the decision of using the 5x8 grid of 40 sensors in the hardware implementation of the real prototype.

In a second step we compare different possible sensor layouts using the same sensor count of 40 sensors and evaluate these. Figure 4.10 shows the three evaluated sensor layouts. The left image of Figure 4.10 shows the equally distributed sensors covering the entire prototype. In the middle we see the 5x8 sensor grid in a more constrained area, covering the entire body of the test person. The third image at the right shows a combination of the previous two layouts covering the upper body and the sides of the bed cover. Underneath the images, the mean F-measure per layout is shown, reaching the highest value of 85.2 % with the upper body coverage layout.

Total coverage

F−measure: 81 %

Length coverage

F−measure: 78 %

Upper body coverage

F−measure: 85.2 %

Figure 4.10.: Layout comparison of three different layout designs with 40 sensors evaluated using the simulation framework. The layout covering the upper body achieves the highest F-measure of 85.2 %.

In order to detect if some sensor data is still redundant, we consider the upper body coverage layout with a growing subset of sensors. The different sensor subsets are depicted in Figure 4.11. Additionally to these sensor layout subsets, Figure 4.12 shows the mean F-measure of the evaluation results. For each sensor layout subset, a leave one subject out cross-validation has been effectuated for real and simulated data. Both are depicted on the same graph. We observe that in general better results are achieved evaluating the real prototype. The simulation data shows a clear gain in F-measure at the layout using a subset of 14 sensors. The real data similarly shows a high slope at the layout using 14 sensors. The mean F-measure values at this point are 84.7 % for the real prototype and 80.4 % for the virtual prototype. Looking up the corresponding 14 sensor layout subset from Figure 4.11, we observe that it represents the minimum configuration covering the whole upper body of the test person as well as areas outside, where the bed cover lies on the bed.

We observe a second similar but smaller jump in F-measure by looking at the simulation data for the subset of 30 sensors. The real data mimics this small peak achieving the highest F-measure with 89.4 %. Looking at the corresponding layout subset with 30 sensors, we observe that the improvement

4.1. Prototyping applications for flexible surfaces through simulation

Figure 4.11.: F-measure of increasing sensor amount using upper body coverage layout with 40 sen-sors. For each sensor layout the mean F-measure is calculated by effectuating a leave one subject out cross-validation for simulated and real data.

of results is generated by adding sensors to the first and last line, covering the shoulders and legs as well as the area lying flat on the bed.

4.1.7. Summary

In Section 4.1 we presented a simulation framework for applications with flexible surfaces such as a shape-sensing fabric. Even before the hardware is available developers can start designing and virtually evaluating their prototype for their individual shape-sensing application.

We proposed and demonstrated the workflow of a prototype designing process with shape-sensing fabrics by virtually planning a sleeping posture detecting bed cover. According to its virtual model we have equipped a bed cover with 40 acceleration sensors and evaluated it with 10 different users.

Comparing the F-measures of 85% for the virtual and 89% for the real-world implementation we val-idate our proposed simulation framework for shape-sensing fabrics. We show further advantages of the simulation framework in terms of analyzing optimization potentials in order to find the suitable trade-off point for the required application. To our knowledge there is no simulation framework which includes soft-body simulation and attaches virtual sensors to them. Through our simulation framework we were able to provide a decision basis for developers when they need to decide on the trade-off between number of sensors, sensor placement and achieved accuracy.

In Section 4.2 the general approach of the simulation framework is demonstrated by using this for planning further shape-sensing applications. Examples of such shape-sensing applications in the field of ambient intelligence could be intelligent clothing which reports on its status (in drawer, washing bin, coat hanger, coat hook, if it is worn correctly, etc.) or an intelligent furniture cover which would detect

4. Simulation framework for designing flexible Smart Environment applications

4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 40

50 60 70 80 90 100

Number of sensors

F−measure (%)

Real data Simulated data

Figure 4.12.: F-measure of layout with increasing sensor number

on which furniture it is placed and provide the user with services such as posture detection, breathing frequency tracking, amount of movement.