## 2 Related Works  
Numerous survey papers on e-health have been published recently [7; 8; 9; 10; 11; 12; 13; 14; 15]. A systematic review of the wearable sensors to monitor human activities is the focus of [7]. The work in [8] surveyed sensors, communication protocols and hardware platforms used in wearable and unobtrusive devices. The authors discussed the following short-range communication protocols: Zigbee, NFC, UWB, and WiFi. However, the security and the algorithms used in the wearable devices were not considered. The authors of [9] summarized the research work in wireless communication (WAN, LAN, and BAN) and briefly discussed energy harvesting for wearables. However, energy-efficiency cannot be achieved by looking at wireless communication only. Innovation in sensor technology, and the low-power design of the integrated circuits for wearables from the system down to the layout should be considered. On the same topic, in [10], wireless communication protocols for a body area network were compared based on four metrics: transmission range, latency, power consumption, security and privacy, and data rate. It was concluded that ZigBee and low-power Bluetooth are the most promising candidates for BAN. The design requirements of the biomedical wearables were the focus of the work presented in [11]. The authors developed factors that should be considered for developing biomedical wearables. Those factors include cost, features of the biomedical signal, human factors, and the ecosystem. The study focused on hardware requirements for four sensors (PPG, EMG, microphone, and IMU) and two embedded systems (Arduino and Raspberry PI). The work lacks discussions of algorithms used to process big IoMT data, energy harvesting, power management techniques which are vital for wearables. A few shortcomings of the previous survey work have been addressed in [12]. The authors reviewed the existing technologies, algorithms, and architectures for the processing of medical data. In particular, the authors described the architecture, technologies, and application of the IoMT, cloud computing IoMT and edge-cloud IoMT. The discussed IoMT technologies are RFID, wireless sensor networks and middleware. The authors considered AI the technology of the cloud computing layer. However, the trend in the IoT is to move AI to the edge and fog layers. The work of [13] \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Complete Name** \\ \hline CNN & Convolution Neural Networks \\ \hline DBN & Deep Belief Network \\ \hline RNN & Recurrent Neural Networks \\ \hline CPU & Central Processing Unit \\ \hline FPGA & Field Programmable Gate Array \\ \hline ASIC & Application Specific Integrated Circuit \\ \hline MPSoCs & Many-core systems-on-chip \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\) \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Explanation** &
\begin{tabular}{c} **Typical Value/Value** \\ **Range** \\ \end{tabular} \\ \hline \(S\) & scaling factor & 1.44 \\ \hline \(x\) & 1-D biomedical signal & NA \\ \hline \(N\) & Signal Dimension & 512 for ECG signal \\ \hline PRD & Percentage Root-mean square Difference & \(\leq\)10\% \\ \hline \(\delta_{L}\) & Isometry constant & 0.25 \\ \hline \(\delta\) & an arbitrary small and positive number & NA \\ \hline RIP & restricted isometry property & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of parameters with their typical value. reviewed existing IoMT monitoring systems and the machine learning algorithms used at the edge layer. The application of edge, fog, and cloud computing in e-health was the focus of the review work described in [15]. The authors surveyed machine learning algorithms for monitoring, classification of biomedical signals and predictions of patient well-being. They further analyzed the pros and cons of edge intelligence for an IoT health care system. However, hardware implementation techniques of machine learning algorithms' target reliability and power efficiency were overlooked.