Body Fat Percentage Prediction Algorithm Based on PSO-ELM Model and BIA

Abstract

Bioelectric impedance analysis (BIA) measures the body fat percentage value compared with the traditional body fat detection method. It has many advantages, such as non-invasive, harmless, cheap, simple operation and rich functional information. The more attention you pay. Aiming at the problem that the prediction results of the extreme learning machine (ELM) regression model are affected by the input parameters, the particle swarm optimization algorithm (PSO) is applied to the ELM, and a body fat percentage prediction method based on the particle swarm optimization extreme learning machine is proposed. The method firstly preprocesses the physiological parameters such as body impedance, height and weight, and constructs a training sample set, then establishes an ELM model, and uses the particle swarm optimization algorithm to optimize the input weights and thresholds in the ELM, thereby establishing NWP-based and PSO-ELM body fat rate prediction model.