计算机科学
卷积神经网络
人工智能
循环平稳过程
活动识别
机器学习
模式识别(心理学)
深度学习
人工神经网络
频道(广播)
计算机网络
作者
Niloy Sikder,Md. Sanaullah Chowdhury,Abu Shamim Mohammad Arif,Abdullah-Al Nahid
出处
期刊:Cornell University - arXiv
日期:2019-09-01
被引量:30
标识
DOI:10.1109/icaee48663.2019.8975649
摘要
Human Activity Recognition (HAR) simply refers to the capacity of a machine to perceive human actions. HAR is a prominent application of advanced Machine Learning and Artificial Intelligence techniques that utilize computer vision to understand the semantic meanings of heterogeneous human actions. This paper describes a supervised learning method that can distinguish human actions based on data collected from practical human movements. The primary challenge while working with HAR is to overcome the difficulties that come with the cyclostationary nature of the activity signals. This study proposes a HAR classification model based on a two-channel Convolutional Neural Network (CNN) that makes use of the frequency and power features of the collected human action signals. The model was tested on the UCI HAR dataset, which resulted in a 95.25% classification accuracy. This approach will help others to conduct further researches on the recognition of human activities based on their biomedical signals.
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