Deep reinforcement learning-based keypoint feature recognition technology for elderly standing posture stability

Abstract

To strengthen the health monitoring of the elderly, reduce the risk of falls, and accurately evaluate dynamic standing stability, this study proposes a key feature recognition technology for standing stability based on deep reinforcement learning. First, deploy high-definition devices to capture standing posture images, use BSCB algorithm to repair image defects, then use SIFT algorithm to extract key posture feature points, and construct a time displacement sequence through bidirectional matching strategy. Next, a reinforcement learning recognition framework based on Deep Q-Network (DQN) is constructed, which combines Long Short Term Memory (LSTM) to extract dynamic dependency relationships. Policy networks and reward functions are used to optimize recognition decisions, and experience replay and target network mechanisms are introduced to improve training stability. The experiment shows that this method has the recognition sensitivity can reach 0.95 under optimal conditions and remains above 0.91 in low-light scenarios, strong environmental adaptability and recognition robustness, providing technical support for predicting and intervening in the risk of falls in the elderly.