Abstract: This review summarizes research advances in sarcopenia screening technologies, focusing on novel assessment methods based on biomechanical indicators such as lower limb muscle strength and plantar pressure, as well as the application potential of wearable devices and machine learning technologies in this field. Advancements in wearable sensor technology have made it possible to continuously and Objectively collect gait, balance, and plantar pressure data in natural conditions. By integrating machine learning algorithms, these devices can extract characteristic patterns associated with sarcopenia from multidimensional data to construct highly accurate risk prediction models. Future research should focus on optimizing sensor design, developing more robust and interpretable algorithmic models, and conducting multicenter clinical studies to advance sarcopenia screening technologies toward home-based, community-based, and personalized applications. These developments will ultimately enable early identification, dynamic monitoring, and effective intervention of sarcopenia, thereby improving the quality of life and health outcomes for the elderly population.

Key words: sarcopenia, lower limb muscle strength, plantar pressure, wearable devices, machine learning