Sochopoulos Andreas, Poliero Tommaso, Ahmad Jamil, Caldwell Darwin G, Di Natali Christian
Department of Advanced Robotics, Istituto Italiano di Tecnologia, 16163, Genova, Italy.
Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), Universita' degli Studi di Genova (UniGe), 16145, Genova, Italy.
Sci Rep. 2025 Mar 31;15(1):10954. doi: 10.1038/s41598-024-81312-2.
Human Activity Recognition (HAR) using wearable sensors has prompted substantial interest in recent years due to the availability and low cost of Inertial Measurement Units (IMUs). HAR using IMUs can aid both the ergonomic evaluation of the performed activities and, more recently, with the development of exoskeleton technologies, can assist with the selection of precisely tailored assisting strategies. However, there needs to be more research regarding the identification of diverse lifting styles, which requires appropriate datasets and the proper selection of hyperparameters for the employed classification algorithms. This paper offers insight into the effect of sensor placement, number of sensors, time window, classifier complexity, and IMU data types used in the classification of lifting styles. The analyzed classifiers are feedforward neural networks, 1-D convolutional neural networks, and recurrent neural networks, standard architectures in time series classification but offer different classification capabilities and computational complexity. This is of the utmost importance when inference is expected to occur in an embedded platform such as an occupational exoskeleton. It is shown that accurate lifting style detection requires multiple sensors, sufficiently long time windows, and classifier architectures able to leverage the temporal nature of the data since the differences are subtle from a kinematic point of view but significantly impact the possibility of injuries.
近年来，由于惯性测量单元（IMU）的可用性和低成本，使用可穿戴传感器进行人体活动识别（HAR）引起了广泛关注。使用IMU的HAR既可以辅助对所执行活动进行人体工程学评估，而且最近随着外骨骼技术的发展，还可以帮助选择精确定制的辅助策略。然而，关于识别不同的举升方式，仍需要更多的研究，这需要合适的数据集以及为所采用的分类算法正确选择超参数。本文深入探讨了传感器放置、传感器数量、时间窗口、分类器复杂度以及用于举升方式分类的IMU数据类型的影响。所分析的分类器是前馈神经网络、一维卷积神经网络和递归神经网络，这些是时间序列分类中的标准架构，但具有不同的分类能力和计算复杂度。当期望在诸如职业外骨骼这样的嵌入式平台上进行推理时，这一点至关重要。结果表明，准确的举升方式检测需要多个传感器、足够长的时间窗口以及能够利用数据时间特性的分类器架构，因为从运动学角度来看差异很细微，但会显著影响受伤的可能性。
