Human motion recognition based on progressive neural architecture search for efficient and interpretable spatiotemporal learning

Human motion recognition (HMR) is an important application area in smart surveillance, medical surveillance, and human-computer interfaces. However, human activities are extremely diverse, and the spatiotemporal modeling is a computationally resource-demanding concept; thus, proper and effective recognition is difficult. Traditional deep learning networks are usually not generated automatically; they are computationally expensive or cannot be used to model a wide variety of motion patterns. The current paper introduces a Progressive Neural Architecture Search (PNAS) neural architecture, PNAS-HMR, that is an automatic human movement recognition system that finds the most effective deep network models without compromising recognition accuracy, computation cost, or interpretability. The given framework comprises a progressive search strategy, where the lightweight network candidates are taken into account, and an upward search strategy is used to construct more complex structures with the help of multi-objective search. The search is directed by a surrogate predictor of performance based on a search using Pareto-optimal architectures that meet accuracy, latency, and performance energy requirements. Assessment was performed using motion data from standard datasets, including NTU RGB + D 120, Kinetics-400, and UCF101. The architectures found were further refined and studied using visualization tools and ablation experiments. Experimental tests show that PNAS-HMR achieves higher recognition accuracy (up to 4%) with lower FLOPs and latency than state-of-the-art methods, by more than 30%. The architectures searched exhibit strong generalization and cross-dataset transferability. PNAS-HMR is an effective combination of neural architecture search and spatiotemporal learning that provides an energy-efficient, interpretable, and scalable framework for real-time human motion recognition in various practical problems.