As the global population over 60 years old is projected to reach 1.4 billion by 2030, traditional labor-intensive care models will concurrently become economically unsustainable. To reduce elderly care costs and improve healthcare outcomes with less labor, we developed and evaluated a structural equation model (SEM) for multimodal sensor system implementation. Using data from 2500 participants in the Medical Expenditure Panel Survey, SEM and Monte Carlo simulations (10000 iterations) were applied to quantify the clinical and economic impacts of sensor-based surveillance. The results demonstrate that multimodal sensor adoption reduces average monthly healthcare costs by 27.5%, from USD 1477 to 1071, yielding savings of USD 406 per user. Hospitalizations decreased by 43% and emergency room visits by 38%, underscoring the clinical benefits of preventive monitoring. However, SEM analysis revealed a weak negative relationship between raw sensor quality and care quality (β = −0.019), indicating that technical accuracy alone is insufficient without seamless integration and user-friendly design. Economic simulation results showed a five-year net present value of USD −13836, with positive returns in only 21.1% of cases, highlighting significant implementation barriers. These results suggest that sensor systems for health care must prioritize interoperability, age-friendly materials, and cost-efficient manufacturing to achieve sustainable adoption in aging societies.