A survey on large-scale unobtrusive BMI estimation for public health

Objective: Body Mass Index (BMI) is a critical indicator for assessing obesity, playing a key role in public health surveillance, chronic disease prevention, and personalized medicine. Traditional BMI estimation methods rely on manual measurements of weight and height; although computationally straightforward, they have significant limitations in large-scale monitoring and unobtrusive assessment scenarios. This study aims to systematically review recent advances in BMI estimation methods based on Artificial Intelligence (AI) and sensing technologies, summarizing their technical characteristics and development trends. Methods: Through a systematic retrieval and analysis of relevant literature from multiple sources, this study categorizes existing BMI estimation methods according to their technical principles and application scenarios. Furthermore, it provides a comparative discussion of the strengths and limitations of the related research. Results: This study indicates that current research primarily focuses on three technical approaches: wireless sensing, contact sensors, and vision-based technologies. All three categories can achieve varying degrees of BMI estimation accuracy across different application scenarios. Wireless sensing methods offer advantages in unobtrusive monitoring; sensor-based methods excel in stability and long-term monitoring; and vision-based methods demonstrate greater potential in information richness and accuracy, albeit with challenges related to privacy and data diversity. In addition, these approaches differ significantly in terms of performance, deployment conditions, and target populations. Conclusion: AI-based BMI estimation methods provide novel solutions for smart health monitoring. However, further research is needed to address challenges such as model generalization, privacy protection, and large-scale real-time deployment. Future development trends point toward multi-modal fusion, enhanced robustness, and the implementation of deployable, unobtrusive health monitoring systems.