ScooterID: Posture-Based Continuous User Identification From Mobility Scooter Rides

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Mobile Computing Pub Date : 2024-10-02 DOI:10.1109/TMC.2024.3473609
Devan Shah;Ruoqi Huang;Nisha Vinayaga-Sureshkanth;Tingting Chen;Murtuza Jadliwala
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Abstract

Mobility scooters serve as a powerful last-mile transportation tool for people with mobility challenges. Given the unique riding behavior and posture of mobility scooter riders, such user-specific mobility scooter ride data has tremendous potential towards the design of continuous user identification and authentication mechanisms. However, there have been no prior research efforts in the literature exploring this unique modality for the design of continuous user identification techniques. To address this gap, this paper proposes ScooterID , the first framework which employs rider posture data collected from cameras on mobility scooters to continuously identify (and authenticate) users/riders. As part of this framework, a machine learning based model comprising of a spatio-temporal Graph Convolutional Network and a body-part-informed encoder is designed to effectively capture a user’s subtle upper-body movements during mobility scooter rides into discriminating embedding vectors. These embeddings can then be used to reliably and continuously identify and authenticate users/riders. Experiments with real-world mobility scooter ride data show that ScooterID achieves high levels of authentication accuracy with few enrollment video samples. ScooterID also performs efficiently on resource-constrained devices (e.g., Raspberry Pis) and is robust against adversarial perturbations to authentication inputs.
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ScooterID:基于姿势的移动滑板车用户连续识别
对于行动不便的人来说,电动滑板车是一种强大的最后一英里交通工具。考虑到机动滑板车使用者独特的骑行行为和姿势,这种针对用户的机动滑板车骑行数据对于设计持续的用户识别和认证机制具有巨大的潜力。然而,在之前的文献中,还没有研究工作探索这种独特的模式来设计连续的用户识别技术。为了解决这一差距,本文提出了ScooterID,这是第一个使用从移动滑板车上的摄像头收集的骑手姿势数据来连续识别(和认证)用户/骑手的框架。作为该框架的一部分,一个基于机器学习的模型由一个时空图卷积网络和一个身体部位信息编码器组成,旨在有效地捕捉用户在移动滑板车上的细微上半身运动,并将其识别为嵌入向量。然后,这些嵌入可以用来可靠地、持续地识别和验证用户/骑手。对现实世界中机动滑板车骑行数据的实验表明,ScooterID在注册视频样本较少的情况下实现了高水平的认证准确性。ScooterID在资源受限的设备(例如,Raspberry Pis)上也能有效地执行,并且对认证输入的对抗性扰动具有鲁棒性。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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