Paper Info
Title
Gait Recognition as a Service for Unobtrusive User Identification in Smart Spaces
Abstract
Recently, Internet of Things (IoT) has raised as an important research area that combines the environmental sensing and machine learning capabilities to flourish the concept of smart spaces, in which intelligent and customized services can be provided to users in a smart manner. In smart spaces, one fundamental service that needs to be provided is accurate and unobtrusive user identification. In this work, to address this challenge, we propose a Gait Recognition as a Service (GRaaS) model, which is an instantiation of the traditional Sensing as a Service (S2aaS) model, and is specially deigned for user identification using gait in smart spaces. To illustrate the idea, a Radio Frequency Identification (RFID)-based gait recognition service is designed and implemented following the GRaaS concept. Novel tag selection algorithms and attention-based Long Short-term Memory (At-LSTM) models are designed to realize the device layer and edge layer, achieving a robust recognition with 96.3% accuracy. Extensive evaluations are provided, which show that the proposed service has accurate and robust performance and has great potential to support future smart space applications.
Year
DOI
Venue
2020
10.1145/3375799
ACM Transactions on Internet of Things
Keywords
DocType
Volume
IoT,RFID,attention-based LSTM,gait recognition,user identification
Journal
1
Issue
ISSN
Citations 
1
2577-6207
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Chengwen Luo119321.49
Jiawei Wu25213.23
Jianqiang Li310212.31
Jia Wang422.06
Weitao Xu510417.52
Zhong Ming600.34
Bo Wei713222.76
Wei Li822725.46
Albert Y. Zomaya95709454.84