# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
## 4 Experimental Results
### ML-Decision Tree Power Management Results  
Decision trees are able to achieve high accuracy levels in ML-based classification-embedded implementations. However, it is necessary to carry out a training process carefully to avoid overfitting. In this sense, the training and validation process was generated by using a dataset of \(1\times 10^{7}\) samples according to the NEWS standard. From the dataset, 80% of data were used for the training process and 20% for the validation process. For the experimental setup, three degrees of freedom are distinguished: (a) variations on PPG-based biosignal measurements (the Worst Case Scenario--WCS is with high variations because the system must decide between saving energy or sampling accurately), (b) supercapacitor's voltage level (low or high), and (c) the \(m\)-number of classes in the ML-DPMS, which corresponds to the sleep period in the MCU. In this sense, two test case scenarios demonstrate the multimodal power management results based on the ML-decision tree approach. Figure 7: Measured supercapacitor voltage recovery after PPG sensing, processing, and BLE transmission. The first scenario considers biosignal measurements with medium to high SpO2, Temp, and HR variations, with a critical behavior in samples 50 to 80 (See Figure 8a-c showing an SpO2 level below 90%, Temperature of 40\({}^{\circ}\) and HR above 120). The supercapacitor voltage level, in Figure 8d, is above 4.0 V with a steady behavior at the beginning, and slow variations from sample 20. Five classes were employed in the experiment according to the following sleep periods: \(m_{1}\) = 5 min, \(m_{2}\) = 7.5 min, \(m_{3}\) = 10 min, \(m_{4}\) = 12.5 min and \(m_{5}\) = 15 min. The ML-decision tree detects the quasi-stationary behavior of the supercapacitor and analyzes the biosignal variations, introducing shorter sleep periods than the traditional uniform power management. See Figure 8e with classification results from \(m_{1}\) to \(m_{3}\) in all the graphs, which means sleep period levels from 5 to 10 min. The results of test scenario 1 demonstrate the efficiency of the ML-decision tree power management. With sufficient energy in the supercapacitor and medium to high variations of the incoming data, the ML algorithm increases the sample period. Notice that during samples 50 to 80, the classification results were even shorter for \(m_{1}\) = 5 min, and \(m_{2}\) = 7.5 min. Figure 9 highlights the red square region of the classification results of Figure 8e, i.e., the samples from 70 to 80. Here, a comparative analysis of the uniform power management strategy with our proposed ML-DPMS is presented. As one can notice, the ML-decision tree power management achieves 12 transmissions, 2.4\(\times\) more than the uniform strategy with a sleep period of 15 min. In terms of energy consumption, the ML-DPMS approach requires 2.34 ] and the uniform power management consumes 1.37 ]. Therefore, one can see that multimodal power management has better energy use, with more transmissions when sensitive events are detected in the biosignal measurements, but with a relatively more energy consumption difference, i.e., 0.97 ],  
Figure 8: ML-decision tree results for test case scenario 1: (**a–c**) SpO2, Temperature and Heart rate measurements; (**d**) Supercapacitor’s voltage; and (**e**) ML-decision tree classification results. * The second test case scenario considers the same unstable behavior on the biosignal measurements as shown in Figure 10a-c. Five classes are also employed with the same range of test case 1. Although the unstable behavior of the biosignal measurements suggests shorter sample periods, the ML-decision tree analyzes the amount of energy stored in the supercapacitor (see the oscillatory behavior of V\({}_{scap}\) in Figure 10d and the red square of Figure 10e). The algorithm chooses the best sleep duty cycle, avoiding the system shutting down. The ML-decision tree algorithm also demonstrates a good balance between energy saving and accuracy.