International Journal of Advanced Research in Science Communication and Technology · Apr 12, 2026
Inner Age Predictor: A Multimodal AI-Based Framework for Biological, Behavioral and Visual Age Estimation
By Pasalkar SSPJ, Shejul AWG
Proper estimation of the aging of human beings has now emerged as a burning problem in contemporary preventive healthcare. Chronological age- although simple to assess does not reflect the actual physiological, metabolic and functional condition of an individual. To overcome this restriction, a Multimodal Inner Age Predictor framework, combinative of three complementary modalities of data, including (1) clinical and biomarker data to estimate biological age by training gradient boosters, (2) continuous wearable and IoT sensor data to predict and maintain real-time physiological and behavioural patterns which are analysed through LSTM and Transformer models, and (3) facial image analysis data A hybrid multimodal fusion layer is used to take the individual modality outputs and combine into one, strong inner age score. This framework is based on a systematic overview of fifteen IEEE- and Springer-peer-reviewed studies, and the framework is designed to be benchmarked with other single-modality frameworks. Experimental simulations show that the proposed system has a Mean Absolute Error (MAE) of 2.4 years and R2 of 0.93, which greatly performs better than unimodal baselines. In addition to accuracy, the framework promotes real-time monitoring health, early detection of chronic diseases, and customised lifestyle suggestions.