Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis

The widespread use of Inertial Measurement Units (IMUs) has transformed human motion analysis, offering key advantages such as low cost, ease of use, broad acquisition range, and unobtrusiveness. IMUs are especially valuable in ecological settings, enabling applications in clinical outcome assessmen...

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Detaylı Bibliyografya
Materyal Türü: Online
Dil:İngilizce
Baskı/Yayın Bilgisi: MDPI - Multidisciplinary Digital Publishing Institute 2025
Konular:
Online Erişim:ONIX_20250812T110751_9783725842278_386
Etiketler: Etiketle
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Özet:The widespread use of Inertial Measurement Units (IMUs) has transformed human motion analysis, offering key advantages such as low cost, ease of use, broad acquisition range, and unobtrusiveness. IMUs are especially valuable in ecological settings, enabling applications in clinical outcome assessment (e.g., tele-rehabilitation), sports performance and injury prevention, and human–robot interaction. Recent advancements in miniaturization, performance, and integration have expanded their potential, especially when combined with artificial intelligence techniques like machine learning and deep learning. The Special Issue, titled “Recent Advances and Applications of Wearable Inertial Sensors in Motion Analysis”, features 18 high-quality publications—14 original research articles, 2 systematic reviews, and 2 communications—exploring the novel uses of wearable IMUs. A strong focus is placed on gait analysis, with contributions addressing trunk acceleration patterns, gait variability, fall risk assessment, and spatio-temporal parameter estimation, even in patients using assistive devices. IMUs also support accurate foot trajectory reconstruction and terrain identification. Other studies examine maternal gait during labor, cardiovascular stress during walking, and AI-enhanced gait phase prediction. Beyond gait, IMUs are used to analyze upper and lower body movements, post-COVID return to running, balance metrics via smartwatches, magnetic interference detection, and activity recognition through gesture analysis. Collectively, these works significantly advance wearable motion analysis research.