Biomechanical application research on cognitive health management in the elderly based on data analysis and intelligent coordination in the age of artificial intelligence

The conventional approach to elder care is no longer able to satisfy the rising need for medical attention for the elderly due to China’s aging population. The demographic trait of “getting old before getting rich” presents a challenge to the distribution of social healthcare resources, as this article first examines the current pattern of changes in the composition of the older population. The community-based “healthcare integration” paradigm of senior care services has emerged as a successful remedy in this regard. Drawing on biomechanical principles, we can envision the community healthcare system as a complex “biomechanical network”. In order to categorize and predict the health data of the elderly, this study constructs a mathematical model akin to analyzing biomechanical forces and movements. By employing methods similar to optimizing structural loads, such as the CART decision tree and support vector machine (SVM) optimization, we enhance the model’s precision. Just as biomechanical systems adapt to varying loads, our model adapts to handle complex health data. By building the optimal classification plane of the support vector machine and adding relaxation variables, the model application solves the classification problem of linearly indivisible data, further enhancing the model’s accuracy and effectiveness, much like how a biomechanical structure self-adjusts to external pressures. In this paper, a geriatric health service platform based on information technology, including big data and the Internet of Things (IoT), is formed. The service system is a tripartite linkage disease management service model that covers the synergistic cooperation of community hospitals, third-party enterprises, and the streets where they are located. A prediction model for common cases, such heart disease, was developed by preprocessing and cleaning the data of 2311 valid samples from the China Geriatrics Center. The dataset was then characterized. The findings demonstrate the model’s high operability and accuracy in predicting health and managing long-term care for older people who are mobility. In the context of an aging society, by integrating biomechanical insights into the design of this healthcare model, the research not only establishes a theoretical foundation for community health care integration but also provides valuable references for implementing digital senior care services and enhancing health management for the elderly in an aging society.