Comparative Analysis of Edge AI Hardware Platforms for Wearable Assistive Technologies

This review paper presents a comparative analysis of AI hardware platforms for edge devices, focusing on their suitability for wearable assistive technology applications. As edge computing in AI continues to expand, numerous hardware solutions have emerged, each optimized for specific performance metrics such as inference speed, energy consumption, and processing capability. By reviewing six recent studies on edge AI hardware, this paper assesses the adaptability of these platforms for energy-constrained environments—crucial for real-time applications such as AI-based wearable devices for visually impaired users. The paper offers key insights into different hardware selections, their performance efficiency and future advancements required to boost the capability of wearable AI solutions.