计算机科学
卷积神经网络
加速度计
活动识别
特征工程
任务(项目管理)
特征提取
模式识别(心理学)
人工智能
特征(语言学)
航程(航空)
深度学习
极限(数学)
数据挖掘
机器学习
数学
语言学
材料科学
管理
经济
复合材料
哲学
数学分析
操作系统
标识
DOI:10.1016/j.asoc.2017.09.027
摘要
With a widespread of various sensors embedded in mobile devices, the analysis of human daily activities becomes more common and straightforward. This task now arises in a range of applications such as healthcare monitoring, fitness tracking or user-adaptive systems, where a general model capable of instantaneous activity recognition of an arbitrary user is needed. In this paper, we present a user-independent deep learning-based approach for online human activity classification. We propose using Convolutional Neural Networks for local feature extraction together with simple statistical features that preserve information about the global form of time series. Furthermore, we investigate the impact of time series length on the recognition accuracy and limit it up to 1 s that makes possible continuous real-time activity classification. The accuracy of the proposed approach is evaluated on two commonly used WISDM and UCI datasets that contain labeled accelerometer data from 36 and 30 users respectively, and in cross-dataset experiment. The results show that the proposed model demonstrates state-of-the-art performance while requiring low computational cost and no manual feature engineering.
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