Research on denoising physiological signals highlights the challenges of motion artifacts during physical activities, particularly for individuals with disabilities. A review of various denoising techniques was conducted to evaluate their computational costs and feasibility for use in wearable systems. A database of physiological signals collected during different exercises revealed that some signals, like skin temperature measurements, are largely unaffected by motion, whereas EEG and ECG signals experience significant interference. Among the activities analyzed, jumping produced the most signal contamination. The study also tested the power consumption associated with different levels of complexity in denoising methods and sampling rates. The goal is to identify efficient denoising techniques that can be implemented on low-power wearable devices designed for monitoring physical activity.