Real-time human activity recognition from accelerometer data using Convolutional Neural Networks
Real-time human activity recognition from accelerometer data using Convolutional Neural Networks
复制标题
DOI：
10.1016/j.asoc.2017.09.027
复制
发表时间：
2018-01-01
影响因子：
8.7
通讯作者：
Ignatov, Andrey
中科院分区：
文献类型：
--
作者：
Ignatov, Andrey
随着各种传感器广泛地嵌入到移动设备中，对人类日常活动的分析变得更加常见和直观。这项任务现在出现在一系列应用中，例如医疗保健监测、健康跟踪或用户自适应系统，其中需要能够识别任意用户的瞬时活动的通用模型。本文提出了一种基于用户独立深度学习的在线人类活动分类方法。我们建议使用卷积神经网络来进行局部特征提取，并结合简单的统计特征来保存时间序列的全局形式的信息。此外，我们还考察了时间序列长度对识别精度的影响，并将其限制在1 S以内，这使得连续的实时活动分类成为可能。在两个常用的WISDM和UCI数据集上，分别包含36个和30个用户的标记加速度计数据，并在交叉数据集实验中评估了该方法的准确性。结果表明，所提出的模型具有最先进的性能，同时需要较低的计算成本，并且不需要人工进行特征工程。(C)2017爱思唯尔B.V.保留所有权利。
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 realtime 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. (C) 2017 Elsevier B.V. All rights reserved.