论文标题
基于卷积神经网络的人类活动识别的惯性传感器信号组合的比较研究
Comparison Study of Inertial Sensor Signal Combination for Human Activity Recognition based on Convolutional Neural Networks
论文作者
论文摘要
人类活动识别（HAR）是许多应用程序的基本构建基础之一，例如安全性，监视，物联网和人类机器人互动。研究界已经开发了各种方法来根据各种输入类型来检测人类活动。但是，该领域的大多数研究都集中在中心应用以外的其他应用上。本文着重于优化输入信号，以最大程度地提高可穿戴传感器的HAR性能。已经提出了基于卷积神经网络（CNN）的模型，并以三个惯性测量单元（IMU）的不同信号组合进行了训练，这些模型表现出了主体的主要手，腿和胸部的运动。结果表明，对于12或更高的模式，信号的K折跨验验精度在99.77至99.98％之间。较低的尺寸信号的性能（除了包含来自胸部和踝关节的信息的信号外，尺寸均较低，表现出73％至85％的精度。
Human Activity Recognition (HAR) is one of the essential building blocks of so many applications like security, monitoring, the internet of things and human-robot interaction. The research community has developed various methodologies to detect human activity based on various input types. However, most of the research in the field has been focused on applications other than human-in-the-centre applications. This paper focused on optimising the input signals to maximise the HAR performance from wearable sensors. A model based on Convolutional Neural Networks (CNN) has been proposed and trained on different signal combinations of three Inertial Measurement Units (IMU) that exhibit the movements of the dominant hand, leg and chest of the subject. The results demonstrate k-fold cross-validation accuracy between 99.77 and 99.98% for signals with the modality of 12 or higher. The performance of lower dimension signals, except signals containing information from both chest and ankle, was far inferior, showing between 73 and 85% accuracy.
