Gait Recognition as a Service for Unobtrusive User Identification in Smart Spaces

Luo, Chengwen, Wu, Jiawei, Li, Jianqiang, Wang, Jia, Xu, Weitao, Ming, Zhong, Wei, Bo, Li, Wei and Y Zomaya, Albert (2020) Gait Recognition as a Service for Unobtrusive User Identification in Smart Spaces. ACM Transactions on Internet of Things, 1 (1). p. 5. ISSN 2577-6207

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Official URL: https://doi.org/10.1145/3375799

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.

Item Type: Article
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 14 Nov 2019 15:57
Last Modified: 23 Apr 2020 13:15
URI: http://nrl.northumbria.ac.uk/id/eprint/41441

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