Data Augmentation and Dense-LSTM for Human Activity Recognition Using WiFi Signal

过度拟合 计算机科学 活动识别 稳健性(进化) 机器学习 信道状态信息 无线 人工智能 数据建模 频道(广播) 模式识别(心理学) 语音识别 人工神经网络 电信 基因 数据库 生物化学 化学
作者
Jin Zhang,Fuxiang Wu,Bo Wei,Qieshi Zhang,Hui Huang,Syed Wajid Ali Shah,Jun Cheng
出处
期刊:IEEE Internet of Things Journal [Institute of Electrical and Electronics Engineers]
卷期号:8 (6): 4628-4641 被引量:103
标识
DOI:10.1109/jiot.2020.3026732
摘要

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual's limb motions in the WiFi coverage area could interfere with wireless signal propagation, that manifested as unique patterns for activity recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carries substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual's activities. Since only recording activities of limited subjects in a certain speed and scale, recent works commonly have a moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep-learning model that caters to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
张叶完成签到,获得积分10
1秒前
DIY101发布了新的文献求助10
2秒前
tangz发布了新的文献求助10
2秒前
山河完成签到,获得积分10
2秒前
鲤鱼问雁完成签到,获得积分10
3秒前
3秒前
jzyy完成签到 ,获得积分10
3秒前
现实的听芹完成签到,获得积分10
3秒前
钟小熊完成签到,获得积分10
3秒前
王金豪完成签到,获得积分10
4秒前
hbb完成签到 ,获得积分10
4秒前
研友_7LMbwn发布了新的文献求助10
4秒前
SciGPT应助饼饼采纳,获得10
4秒前
xinxiangshicheng完成签到 ,获得积分10
5秒前
刘奶奶的牛奶完成签到,获得积分10
5秒前
清爽盼秋完成签到,获得积分10
5秒前
sunyanghu369发布了新的文献求助10
5秒前
6秒前
清蒸可达鸭完成签到,获得积分10
6秒前
锥子完成签到,获得积分10
6秒前
白色的风车发布了新的文献求助200
6秒前
机会啊完成签到,获得积分10
7秒前
123456完成签到,获得积分10
7秒前
迷路的鸽子完成签到,获得积分10
7秒前
苹果王子6699完成签到 ,获得积分10
8秒前
hani完成签到,获得积分10
9秒前
10秒前
激动的xx关注了科研通微信公众号
10秒前
11秒前
柠檬发布了新的文献求助10
12秒前
夸克的诗意完成签到,获得积分10
12秒前
无花果应助yy湫采纳,获得10
12秒前
12秒前
xlj完成签到,获得积分10
12秒前
清欢完成签到,获得积分10
12秒前
土土完成签到,获得积分10
13秒前
滕遥完成签到,获得积分10
13秒前
木木完成签到,获得积分10
14秒前
Oz完成签到,获得积分10
14秒前
迷人耗子精完成签到,获得积分10
14秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
A new approach to the extrapolation of accelerated life test data 1000
Coking simulation aids on-stream time 450
北师大毕业论文 基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 390
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Robot-supported joining of reinforcement textiles with one-sided sewing heads 360
Novel Preparation of Chitin Nanocrystals by H2SO4 and H3PO4 Hydrolysis Followed by High-Pressure Water Jet Treatments 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4015859
求助须知:如何正确求助?哪些是违规求助? 3555835
关于积分的说明 11318981
捐赠科研通 3288954
什么是DOI,文献DOI怎么找? 1812355
邀请新用户注册赠送积分活动 887882
科研通“疑难数据库(出版商)”最低求助积分说明 812027