# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 3 Related Work and Motivation  
The use of FPGAs by researchers for physiological monitoring in wearable devices is increasing to meet the demand for higher computational capabilities, as shown in Figure 2. We evaluated various survey and review studies related to the objective of this work. It has been observed that several notable studies have reviewed energy optimization strategies, with a particular emphasis on micro-controllers and CPU-based systems, while some other research has investigated these optimizations more broadly without centering on a specific platform. For instance, the authors of [41] comprehensively reviewed low-power techniques (LPTs) through a literature review for WMDs and proposed a taxonomy. However, their work does not address FPGA-based wearable devices and corresponding LPTs. Similarly, in [42], the authors reviewed the various energy-efficient strategies to minimize power consumption in the Internet of Wearable Things (IoWT) without focusing on a particular platform. The authors did not mention any FPGA-specific optimization technique at all. In a similar fashion, the authors of [43] comprehensively reviewed LPTs through a literature review for WMDs and proposed a taxonomy. However, their work does not address FPGA-based WMDs or their corresponding LPTs. Figure 2: Number of papers published on FPGA-based wearable medical devices in last 5 years. Furthermore, in [44], the authors conducted a review of various edge computing architectures in the healthcare domain. These architectures encompass operations like authentication, encryption, classification, and prediction. While the paper briefly mentions the energy optimization topic, it does not delve into a detailed discussion of LPTs and their underlying principles. Additionally, FPGA-specific LPTs were beyond the scope of this survey. In another approach, various energy-efficient solutions for IoWT applications were examined in [45]. The authors discussed common power optimization strategies. However, the paper did not review the energy-efficient utilization of FPGAs in such applications. Likewise, the authors of [46] conducted a review on energy optimization for edge computing in mobile devices. While they touched upon several aspects of energy optimization at the device, application, and transmission levels, their primary focus remained on energy-efficient computation offloading and the corresponding execution mechanisms across various cloud environments. Notably, the review did not delve into application-specific optimization strategies for FPGA-based WMDs in physiological monitoring. In another work [47], the challenges faced by wearable devices, including energy efficiency, were reviewed and discussed. However, this work did not delve into energy optimization techniques, their classification, or the use of FPGAs in wearables. Furthermore, the authors of [48] classified energy optimization strategies into hardware (HW) and software (SW) levels. However, FPGA-based approaches were not explored. Last but not least, the authors of [49] comprehensively documented and classified energy optimization strategies, along with different architectural approaches. While their work is commendable, it primarily focuses on human context recognition applications and does not delve into FPGA-based optimization. On the other hand, some reviews and surveys briefly mentioned energy optimization approaches. However, they did not conduct a thorough investigation or discussion of these strategies and their corresponding implications, as this was not the main focus of their studies. For example, in [50], the authors reviewed the implementation of real-time machine learning (ML) algorithms on FPGAs and systems-on-chips (SoCs) within the biomedical domain. They investigated how different researchers optimize their ML models to overcome resource limitations for a given accuracy.