As the global population ages, older adults face increased fall risks due to physical decline, leading to severe consequences such as fractures and head injuries. Dynamic balance is crucial for maintaining stable gait and preventing falls, yet age-related factors like muscle weakness and vision impairments make it more challenging. Traditional balance assessment methods, such as the Berg Balance Scale (BBS) and Short Physical Performance Battery (SPPB), primarily focus on static balance and rely on subjective observations, leading to inconsistent evaluations. This study developed an innovative balance assessment system that integrates pressure sensing technology and center of gravity (COG) trajectory tracking. Using standardized stepping tasks inspired by the stepping patterns of the Japan Square Step Association, the system ensures data stability and consistency. The experiment recorded total stepping time and COG sway amplitude and compared these metrics with traditional balance assessment results. Due to the complexity and time requirements of the original BBS, a simplified version (SF-BBS) was adopted alongside the SPPB to facilitate efficient screening of older adults’ balance capabilities, reducing the burden on participants. Results showed that the system effectively reflects dynamic balance abilities in older adults, with total stepping time and COG sway amplitude strongly correlating with SF-BBS and SPPB outcomes. This method offers a reliable and objective tool for dynamic balance assessment, supporting fall risk pre-diction and rehabilitation program development.