Foot-Step Input Method for Operating Information Devices While Jogging

Yamamoto, Tetsuya; Tsukamoto, Masahiko; Yoshihisa, Tomoki

doi:10.1109/saint.2008.97

Cited by 21 publications

(10 citation statements)

References 3 publications

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“…These work mostly use electronic motion recording (MR) devices such as MR100 wearable sensor [31], ZSTAR [31,39], ADXL202JQ accelerometers [28], MMA7260 [36], etc. These work analyze the accelerometer reading by attaching such MR sensors at different location of the body such as waist [1,28,31,36] (device wore in a belt), lower leg [12,38], shoe [7,18,33,47], pockets (chest/hip) [13,46], upper limb/forearm [13], gloves [21,34,37], and so on. In most of these work, the MR device was tied on the particular parts of the body as most of these devices were not wearable.…”

Section: Related Work On Gait Biometricsmentioning

confidence: 99%

ZEMFA: Zero-Effort Multi-Factor Authentication based on Multi-Modal Gait Biometrics

Shrestha

Mohamed

Saxena

2019

2019 17th International Conference on Privacy, Security and Trust (PST)

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Zero-interaction authentication (ZIA) refers to a form of user-transparent login mechanism using which a terminal (e.g., a desktop computer) can be unlocked by the mere proximity of an authentication token (e.g., a smartphone). Given its appealing usability, ZIA has already been deployed in many real-world applications. However, ZIA contains one major security weakness -unauthorized physical access to the token, e.g., during lunch-time or upon theft, allows the attacker to have unfettered access to the terminal. In this paper, we address this gaping vulnerability with ZIA systems by (un)locking the authentication token with the user's walking pattern as she approaches the terminal to access it. Since a user's walking or gait pattern is believed to be unique, only that user (no imposter) would be able to unlock the token to gain access to the terminal in a ZIA session. While walking-based biometrics schemes have been studied in prior literature for other application settings, our main novelty lies in the careful use of: (1) multiple sensors available on the current breed of devices (e.g., accelerometer, gyroscope and magnetometer), and (2) multiple devices carried by the user (in particular, an "in-pocket" smartphone and a "wrist-worn" smartwatch), that all capture unique facets of user's walking pattern. Our contributions are three-fold. First, we introduce, design and implement WUZIA ("Walk-Unlock ZIA"), a multi-modal walking biometrics approach tailored to enhance the security of ZIA systems (still with zero interaction). Second, we demonstrate that WUZIA offers a high degree of detection accuracy, based on multisensor and multi-device fusion. Third, we show that WUZIA can resist active attacks that attempt to mimic a user's walking pattern, especially when multiple devices are used. IntroductionZero-interaction authentication (ZIA) [8] represents a rapidly emerging paradigm, in which a verifier device authenticates a prover device in physical proximity of the verifier while requiring no interaction by the user of the prover device. The user, carrying the prover, usually just walks towards the verifier and the verifier gets unlocked automatically. In this approach, the prover and verifier devices pre-share a security association, and simply execute a challenge-response based protocol for the verifier to authenticate the prover.

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Section: Related Work On Gait Biometricsmentioning

confidence: 99%

ZEMFA: Zero-Effort Multi-Factor Authentication based on Multi-Modal Gait Biometrics

Shrestha

Mohamed

Saxena

2019

2019 17th International Conference on Privacy, Security and Trust (PST)

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“…When walking/running, arbitrary gestures are more difficult because the feet are busy in the gait cycle. Instead, the movement itself is often used as replacement, for example by mapping the realworld movement to movement in virtual environments, or by using different walking patterns to issue commands [Yamamoto et al 2008].…”

Section: Posementioning

confidence: 99%

“…Deictic & Manipulative [Templeman et al 1999] [Iwata et al 2001] [Darken et al 1997 Intrinsic Semaphoric [Yamamoto et al 2008] Deictic & Manipulative [Choi and Ricci 1997], [Paradiso et al 2000] Implicit [Frey 2007], [Gafurov et al 2011], [Hockman et al 2009], [Huang et al 2006], [Lécuyer et al 2011], [Moens et al 2010], [Morley Jr et al 2001] Extrinsic Semaphoric [Han et al 2011] Implicit [Bränzel et al 2013], [Hu et al 2011], [Orr and Abowd 2000], [Watanabe et al 2005] are inherently bad for this purpose, but rather, because of lack of training [Garcia and Vu 2011]. Future work must take these learning effects into account, allowing users to become sufficiently familiar with the input device for a fair comparison with hand interfaces.…”

Section: Mediatedmentioning

confidence: 99%

The Feet in Human--Computer Interaction

et al. 2015

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Foot-operated computer interfaces have been studied since the inception of Human-Computer Interaction. Thanks to the miniaturisation and decreasing cost of sensing technology, there is an increasing interest exploring this alternative input modality, but no comprehensive overview of its research landscape. In this survey, we review the literature on interfaces operated by the lower limbs. We investigate the characteristics of users and how they affect the design of such interfaces. Next, we describe and analyse foot-based research prototypes and commercial systems in how they capture input and provide feedback. We then analyse the interactions between users and systems from the perspective of the actions performed in these interactions. Finally, we discuss our findings and use them to identify open questions and directions for future research.

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“…1. For instance, for detecting abnormal gaits [25,26], for providing foot motion to the PC as an alternative way of input [27,28], Apple and Nike jointly developed a smart shoe that enables the Nike+ footwear to communicate with iPod to provide pedometer functions [29]. Although the motion recording and detecting sensors in the aforementioned shoes were mainly intended for other purposes, it is possible to extend their functionality for periodic re-verification of identity too.…”

Section: Previous Work and Applicationmentioning

confidence: 99%

User authentication based on foot motion

Gafurov

Bours

Snekkenes

2011

SIViP

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In nearly all current systems, user authentication mechanism is one time and static. Although such type of user authentication is sufficient for many types of applications, in some scenarios, continuous or periodic re-verification of the identity is desirable, especially in high-security application. In this paper, we study user authentication based on 3D foot motion, which can be suitable for periodic identity re-verification purposes. Three-directional (3D) motion of the foot (in terms of acceleration signals) is collected using a wearable accelerometer sensor attached to the ankle of the person. Ankle accelerations from three directions (up-down, forward-backward and sideways) are analyzed for person authentication. Applied recognition method is based on detecting individual cycles in the signal and then finding best matching cycle pair between two acceleration signals. Using experimental data from 30 subjects, obtained EERs (Equal Error Rates) were in the range of 1.6-23.7% depending on motion directions and shoe types. Furthermore, by combining acceleration signals from 2D and 3D and then applying fusing techniques, recognition accuracies could be improved even further. The achieved performance improvements (in terms of EER) were up to 68.8%.

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Foot-Step Input Method for Operating Information Devices While Jogging

Cited by 21 publications

References 3 publications

ZEMFA: Zero-Effort Multi-Factor Authentication based on Multi-Modal Gait Biometrics

ZEMFA: Zero-Effort Multi-Factor Authentication based on Multi-Modal Gait Biometrics

The Feet in Human--Computer Interaction

User authentication based on foot motion

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