# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
###### Abstract  
Precision medicine is now evolving to include internet-of-wearable-things (IoWT) applications. This trend requires the development of novel systems and digital signal processing algorithms to process large amounts of data in real time. However, performing continuous measurements and complex computational algorithms in IoWT systems demands more power consumption. A novel solution to this problem consists in developing energy-aware techniques based on low-power machine learning (ML) algorithms to efficiently manage energy consumption. This paper proposes a multimodal dynamic power management strategy (DPMS) based on the ML-decision tree algorithm to implement an autonomous IoWT system. The multimodal approach analyzes the supercapacitor storage level and the incoming biosignal statistics to efficiently manage the energy of the wearable device. A photoplethysmography (PPG) sensing prototype was developed to evaluate the proposed ML-DPMS programmed in a Nordic nRF52840 processor. The experimental results demonstrate an IoWT system's low consumption of 25.74 J, and a photovoltaic solar power generation capacity of 380 mW. The proposed ML-DPMS demonstrates a battery life extension of 3.87\(\times\), i.e., 99.72 J of energy harvested, which represents the possibility to achieve at least 2.4\(\times\) more data transmissions, in comparison with the widely used uniform power management approach. In addition, when the supercapacitor's energy is compromised, the decision tree technique achieves a good energy conservation balance consuming in the same period of time 39.6\(\%\) less energy than the uniform power approach. a 20  
to measure and transmit data continuously. Instead, it can go to sleep for long periods of time and periodically wake up to sample the biological signs. Once a sensitive event is detected, the sensor can then stay active to acquire more data and transmit it to the cloud. This event-driven pattern is accurate and may not miss any event occurrences, as long as the wake-up period is short enough. However, when the system operates in this mode, its energy consumption increases and fluctuates randomly, making it hard to predict if enough energy is preserved in the storage system for the device to wake up and sense cyclically. In the past, diverse dynamic power management strategies (DPMS) and energy harvesting approaches have been proposed to enhance the energy efficiency of wearable healthcare devices [6; 16; 17; 18; 19]. In [20; 21; 22], DPMS reduced the energy consumption of healthcare devices, identifying the duration and timing of sleep cycling. Moreover, [23] recommends artificial neural networks (ANN) to estimate DPMS wake-up schedules to save energy. Task offloading is another energy-efficiency perspective to reduce the power consumption on wearable devices [24]. However, these strategies rely on cloud services for complex computational operations, which depend on stable internet connectivity [25]. Decision tree (DT) algorithms are a subset of machine learning (ML) that can be used for implementing low-cost hardware implementations [26]. This is because DT uses logical comparators only, which are efficiently synthesized by compilation libraries. On the other hand, neural networks (or classical embedded implementation [27]) and other approaches perform arithmetic operations such as additions, multiplications, or even more complex operations depending on the activation function. For example, [28] discusses the computational complexity of hardware implementations of decision trees and their usage in data classification. The hardware implementation of DT with 8 trees depth requires 257\(\times\) fewer MAC (multiply-accumulate) operations compared to an 8-layer CNN and 43\(\times\) fewer MAC operations compared to a 5-layer CNN. This reduction in MAC operations results in a lower area and power consumption implementation [28]. In this paper, a multimodal power management strategy is proposed for intelligent energy use in IoWT systems.