# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Minimizing Transmission Energy Costs
#### 5.2.2 Compressed Sensing  
Compressed sensing (CS) [106], also known as compressive sensing or sparse sampling, is a signal processing method which efficiently captures and reconstructs signals using fewer samples than typically required, known as sub-Nyquist sampling [107]. This approach reduces the need for continuous sampling and the associated communication load, which can frequently drain the battery [49]. This technique takes advantage of the fact that many natural signals are sparse or compressible in certain domains. By utilizing fewer measurements, compressed sensing can greatly decrease the data required for accurate signal reconstruction, which in turn can significantly lower the transmission load and reduce power consumption. By leveraging this inherent sparsity in most physiological signals, CS becomes a powerful tool compared with conventional compression techniques. The authors of [108] introduced a low-complexity hardware implementation of CS on an FPGA for reconstructing compressively sensed signals using the matching pursuit (MP) algorithm. The proposed hardware design was implemented on the programmable logic (PL) part of a Zybo board, achieving a significant reduction in computational time. It was 75\(\times\) faster than the processing system (PS) implementation on the same board at the cost of extra resources of FPGA. In [109], the authors also implemented multichannel EEG compressive sensing on an FPGA to optimize power consumption in wireless sensors and enhance real-time performance. The system can collect, compress, transmit, and reconstruct EEG signals in real time. Moreover, the authors used a binary permutation block diagonal (BPBD) sensing matrix due to its low computational complexity and resource \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Strategy** & **Description** & **Advantages** & **Constraints** \\ \hline \multirow{2}{*}{Data compression} & Compress the data in lossy or lossless fashion to reduce the transmission load & Reduction in power required for transmission. Reduction in storage requirement. Reduction in power required for both acquisition and transmission. Reduction in storage requirement. & Trade-off between power saved from transmission vs. spent for compression \& reconstruction. \\ \hline \multirow{2}{*}{Compressed sensing} & Efficient acquisition and reconstruction of signals using fewer samples than typically needed & Reduction in power required for both acquisition and transmission. Reduction in storage requirement. & Power saving vs. accuracy trade-off \\ \hline \multirow{2}{*}{Compressed learning} & Focuses on directly inferring information from compressed measurements using machine learning tools & Eliminate the resource and power consumption required for reconstruction. & Additional design effort for optimizing sensing and inference with deep learning during training \\ \hline \multirow{2}{*}{Power-aware transmission} & Adjusting the transmission load based on the current battery level & Maintain availability of critical data by prolonging the battery lifetime. & Trade-off: power consumption vs. accuracy \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimization strategies for minimizing transmission energy costs. efficiency, which helped reduce the power overhead associated with the compressive sensing processes. Compressive sensing reduces the power consumed for data transmission by collecting data in a compressed manner. This is because fewer samples are needed to represent the signal, which translates to less data being transmitted. However, this process requires additional computations for both compression at the front end and reconstruction at the receiving end, which in turn demand extra resources and power. Therefore, there is a trade-off between the power saved from transmitting less data and the power spent on compression and reconstruction.