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Inertial sensors have become significant tools for assessing human movement due to their ability to provide precise data on acceleration, velocity, and orientation in a non-invasive manner. This bibliometric analysis examines the evolving landscape of scientific literature on the use of Inertial Measurement Units (IMUs) in sports over the past two decades. Quantitative methods were employed to analyze 12,620 documents retrieved from the Scopus and Web of Science databases, aiming to identify research trends, key authors, and influential publications. A significant and rapid growth in scientific production appeared, with a strong and growing citation impact (h-index = 192; +27% of papers above the field average), led by the USA and China. Thematic analysis identified “biomechanics” as a basic theme, “wearable sensors” and “machine learning” as core themes, and “gait”, “balance”, and “physical activity” as more developed topics. “Sensor fusion”, “activity recognition”, and “deep learning” emerge as the most impactful themes in terms of citations. In contrast, high-frequency terms such as “sensor” and “wearable” exhibit a lower return. A shift towards deep learning and human activity recognition confirms the growing integration of artificial intelligence in IMU-based movement pattern recognition, performance analysis, injury prevention, and rehabilitation monitoring.
Caprioli et al. (Wed,) studied this question.