基于高性能1D CNN模型的复杂人类活动识别

Raman Maurya, T. Teo, Shi Hui Chua, Hwang-Cherng Chow, I-Chyn Wey
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引用次数: 1

摘要

人体活动识别(HAR)是一门新兴的科学研究领域,在医疗卫生、社会科学、人机交互等领域有着广泛的应用。在许多情况下,人类进行非常复杂的身体活动,需要进行跟踪,以改善福祉、生活质量和健康。本文提出了一种利用三轴加速度计传感器数据,基于一维(1D) CNN模型的复杂HAR算法。传感器数据是从智能手表上收集的,用于研究三种复杂的人类活动,即学习、玩游戏和手机滚动。一维cnn在执行HAR时具有较高的精度和较低的计算复杂度。所提出的1D CNN模型在一个自己准备的数据集上用Python进行训练和优化。调整后的模型的准确率为98.28%。一项初步研究显示,建议的模式可以有效地识别拟进行的活动,作为扩展HAR地区未来工作的基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Complex Human Activities Recognition Based on High Performance 1D CNN Model
Human activity recognition (HAR) is an emerging scientific research field that has wide area of applications in different fields such as healthcare, social-sciences and human-computer interaction etc. In many cases, humans perform very complex physical activities that needs to be tracked in order to improve well-being, quality of life and health. In this study, a method for complex HAR based on One dimensional (1D) CNN model using tri-axis accelerometer sensor data was proposed. The sensor data was collected from a smartwatch for three complex human activities which are studying, playing games and mobile scrolling. 1D CNNs provides high accuracy as well as less computational complexity in performing HAR. The proposed 1D CNN model was trained and optimized on a self-prepared dataset in Python. The adapted model provides an accuracy of 98.28 %. A preliminary study shows that the proposed model could effectively recognize the intended activities as a baseline for extending future work in the HAR area.
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