Characterizing Human Box-Lifting Behavior Using Wearable Inertial Motion Sensors
Characterizing Human Box-Lifting Behavior Using Wearable Inertial Motion Sensors
复制标题
DOI：
10.3390/s20082323
复制
发表时间：
2020-04-01
期刊：
影响因子：
3.9
通讯作者：
Novak, Domen
中科院分区：
文献类型：
--
作者：
Hlucny, Steven D.;Novak, Domen
虽然有几项研究使用可穿戴传感器来分析人体举重，但这通常只以有限的方式进行。在这项概念验证研究中，我们使用可穿戴惯性测量传感器研究了离线升降机特性的多个方面：检测升降机的开始和结束，并对物体的垂直运动、所用姿势、物体的重量以及所涉及的不对称性进行分类。另外，计算提升持续时间、从提升器到物体的水平距离、物体的垂直位移和不对称角度作为提升参数。24名健康的参与者在佩戴商业惯性测量系统的同时进行了两次重复的30种不同的主要升降机。这些试验的数据用于开发、训练和评估所提出的升力特性算法。在所有1489次提升试验中，提升检测算法的开始时间误差为0.10 s +/- 0.21 s，结束时间误差为0.36 s +/- 0.27 s，没有错过提升。对于姿势，不对称，垂直运动和重量，我们的分类器分别实现了96.8%，98.3%，97.3%和64.2%的准确率，自动检测电梯。平均而言，垂直高度和位移估计值在参考值的25厘米范围内。一些移位测量的水平距离与预期的差异很大（高达14.5 cm），但非常一致。估计的不对称角同样精确。在未来，这些概念验证离线算法可以扩展和改进，以实时工作。这将使它们能够用于实时健康监测和辅助设备反馈等应用。
Although several studies have used wearable sensors to analyze human lifting, this has generally only been done in a limited manner. In this proof-of-concept study, we investigate multiple aspects of offline lift characterization using wearable inertial measurement sensors: detecting the start and end of the lift and classifying the vertical movement of the object, the posture used, the weight of the object, and the asymmetry involved. In addition, the lift duration, horizontal distance from the lifter to the object, the vertical displacement of the object, and the asymmetric angle are computed as lift parameters. Twenty-four healthy participants performed two repetitions of 30 different main lifts each while wearing a commercial inertial measurement system. The data from these trials were used to develop, train, and evaluate the lift characterization algorithms presented. The lift detection algorithm had a start time error of 0.10 s +/- 0.21 s and an end time error of 0.36 s +/- 0.27 s across all 1489 lift trials with no missed lifts. For posture, asymmetry, vertical movement, and weight, our classifiers achieved accuracies of 96.8%, 98.3%, 97.3%, and 64.2%, respectively, for automatically detected lifts. The vertical height and displacement estimates were, on average, within 25 cm of the reference values. The horizontal distances measured for some lifts were quite different than expected (up to 14.5 cm), but were very consistent. Estimated asymmetry angles were similarly precise. In the future, these proof-of-concept offline algorithms can be expanded and improved to work in real-time. This would enable their use in applications such as real-time health monitoring and feedback for assistive devices.