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Binary classification of running fatigue using a single inertial measurement unit

机译:使用单个惯性测量单元进行疲劳的二进制分类

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The popularity of running has increased in recent years. A rise in the incidence of running-related overuse musculoskeletal injuries has occurred parallel to this. This study investigates the capability of using data from a single inertial measurement unit (IMU) to differentiate between running form in a non-fatigued and fatigued state. Data was captured from an IMU placed on the lumbar spine, right shank and left shank in 21 recreational runners (10 male, 11 female) during separate 400m running trials. The trials were performed prior to and following a fatiguing protocol. Following stride segmentation, IMU signal features were extracted from the labelled (non-fatigued vs fatigued) sensor data and used to train both a Global and Personalised classifier for each individual IMU location. A single IMU on the Lumbar spine displayed 75% accuracy, 73% sensitivity and 77% specificity when using a Global Classifier. A single IMU on the Right Shank displayed 100% accuracy, 100% sensitivity and 100% specificity when using a Personalised Classifier. These results indicate that a single IMU has the potential to differentiate between non-fatigued and fatigued running states with a high level of accuracy.
机译:近年来跑步的普及率增加。与其发生的与伴侣过度肌肉骨骼损伤发生的发生率的增加并平行于此。本研究研究了使用来自单个惯性测量单元(IMU)的数据的能力来区分在非疲劳和疲劳状态下的运行形式。在分开的400M运行试验期间,在21名休闲跑车(10名男性,11名女性)上放置在腰椎,右胫骨和左柄的IMU中捕获了数据。试验在疲劳方案之前和之后进行。在步幅分割之后,从标记的(非疲劳VS疲劳)传感器数据中提取IMU信号特征,并用于培训每个IMU位置的全局和个性化分类器。使用全局分类器时,腰椎上的单个IMU显示75 %的精度,73 %的灵敏度和77 %特异性。在使用个性化分类器时,右侧柄上的单个IMU显示100 %的精度,100 %的灵敏度和100 %特异性。这些结果表明,单个IMU有可能区分具有高精度的非疲劳和疲劳的运行状态。

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