Exploring Trends and Clusters in Human Posture Recognition Research: An Analysis Using CiteSpace

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Abstract
This study delves into interdisciplinary research directions in human posture recognition, covering vision-based and non-vision-based methods. Visually analyzing 3066 core research papers published from 2011 to 2024 with CiteSpace software reveals knowledge structures, research topics, key documents, trends, and institutional contributions. In-depth citation analysis identified 1200 articles and five significant research clusters. Findings show that in recent years, deep learning and sensor-based methods have dominated, significantly improving recognition accuracy, like the deep learning-based posture recognition method achieving 99.7% verification set accuracy with a 20-ms delay in a controlled environment. Logarithmic growth analysis of annual publications, supported by logistic model fitting, indicates the field’s maturation since 2011, with a shift from early simple applications of traditional and deep learning algorithms to integrating interdisciplinary approaches for problem-solving as the field matures and a predicted decline in future breakthroughs. By integrating indicators like citation bursts, degree centrality, and sigma, the research identifies interdisciplinary trends and key innovation directions, showing a transition from traditional to deep learning and multi-sensor data fusion methods. The integration of biomechanics principles with engineering technologies highlights new research paths. Overall, this study offers a systematic overview to identify gaps, trends, and innovation directions, facilitating future research and providing a roadmap for innovation in human posture recognition.

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