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<p>Wearable technology has become increasingly important for health monitoring, sports performance, and ergonomic assessments because it enables continuous, non-invasive, and real-time tracking of physiological and biomechanical signals in real-world environments, overcoming limitations of laboratory-based assessments. This paper presents the development, testing, and initial study of a sensor-embedded loose garment designed for motion analysis using conductive ink. Sensors were strategically placed across key areas of the T-shirt to capture comprehensive motion data from the torso. Positioned on the chest, shoulders, ribcage, and lower torso, these sensors detect detailed movements. The study evaluates various sensor combinations with four classifiers—XGBoost, RandomForest, SVM, and K-Nearest Neighbors—using data from ten sensor locations analyzed with three holdout methods (20–80%, 30–70%, and 50–50%). Results underscore the impact of specific sensor placements, with combinations on the shoulder, ribcage, and abdomen yielding the highest accuracy. This work advances textile-based motion recognition, showing the potential for wearable technology to distinguish among eight movements in a loose garment.</p>

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Optimized sensor-embedded loose garment for accurate motion detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月6日

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  • Mohamed Elgendi,
  • Tomasz Raczyński,
  • Alexander Shokurov,
  • Daniel Janczak,
  • Małgorzata Jakubowska,
  • Carlo Menon

摘要

Wearable technology has become increasingly important for health monitoring, sports performance, and ergonomic assessments because it enables continuous, non-invasive, and real-time tracking of physiological and biomechanical signals in real-world environments, overcoming limitations of laboratory-based assessments. This paper presents the development, testing, and initial study of a sensor-embedded loose garment designed for motion analysis using conductive ink. Sensors were strategically placed across key areas of the T-shirt to capture comprehensive motion data from the torso. Positioned on the chest, shoulders, ribcage, and lower torso, these sensors detect detailed movements. The study evaluates various sensor combinations with four classifiers—XGBoost, RandomForest, SVM, and K-Nearest Neighbors—using data from ten sensor locations analyzed with three holdout methods (20–80%, 30–70%, and 50–50%). Results underscore the impact of specific sensor placements, with combinations on the shoulder, ribcage, and abdomen yielding the highest accuracy. This work advances textile-based motion recognition, showing the potential for wearable technology to distinguish among eight movements in a loose garment.