# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
## 5 Discussion  
A comparative analysis with other similar studies is presented in Table 1 to highlight the impact of the proposed ML-DPMS energy-saving technique for IoWT, which differs from previous studies [6; 10; 14; 16; 36]. In particular, [6; 14] implement PPG signal analysis with intelligent processing, but without energy harvesting.  
In [10], a Q-PPG methodology based on a deep temporal convolutional network (TCN) was used to predict the user's heart rate (HR) using raw PPG and acceleration data in a low-cost, wireless wrist-worn device. The implementation also included intelligent processing, resulting in low power consumption. However, a one-to-one comparison cannot be made because the methodology was limited to real-time HR predictions and did not include the processing of a power management strategy. In fact, both strategies could be complementary in order to improve the power consumption performance. Likewise, Ref. [16] demonstrates lower power consumption than our study. However, this is due to the absence of a radio device, which is the component with the highest energy demand. The proposed multimodal power management aims to manage data interpretation and transmission following the IoWT paradigm.  
The work in [36] also presents an algorithm for dynamic power management; however, it does not incorporate the multimodal approach for analyzing biosignal statistics and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & [6] & I[10] & I[14] & I[36] & I[16] & \begin{tabular}{c} **This** \\ **Work** \\ \end{tabular} \\ \hline \begin{tabular}{c} **Management** \\ **Technique** \\ **Sensor** \\ **Processical** \\ **Processical** \\ **Storage** \\ **Power** \\ **Generation** \\ **Power** \\ **Consumption** \\ **Energy** \\ **Source** \\ \end{tabular} & \
\begin{tabular}{c} Threshold \\ Decision \\ Network \\ \end{tabular} &
\begin{tabular}{c} Temporal \\ Convolutional \\ **Network** \\ **PPCG** \\ **Blu  
supercapacitor energy availability. In contrast, our ML-DPMS algorithm is implemented in an embedded MCU device, enabling real-time decision-making and adaptation of the transmission duty cycle. Our experimental results demonstrate accurate operation for PPG biosignal measurements with high variability and supercapacitor voltage fluctuations which represents an attractive approach that other studies have not yet incorporated. Furthermore, the proposed prototype with multimodal power management achieves a total consumption of 25.74 J per day for uninterrupted operation.  
On the other hand, other works have been proposed to provide solutions in the IoT field [37; 38; 39; 40; 41; 42]. For example, Ref. [37] proposes a new hybrid power management strategy for use in wireless sensor networks, Ref. [38] proposes an AI-based IoT system with power management blocks. Moreover, a power management unit using solar energy harvesters for wireless sensor nodes is presented in [39]. Likewise, a strategy to achieve energy efficiency in wireless sensor networks can be found in [40]. Additionally, strategies for improving wireless sensor network performance can be seen in [41; 42].