User’s Authentication Using Information Collected by Smart-Shoes

Brombin, Luca; Gambini, Margherita; Gronchi, Pietro; Magherini, R.; Nannini, Lorenzo; Pochiero, Amedeo; Sieni, Alessandro; Vecchio, Alessio

doi:10.1007/978-3-030-34833-5_21

Cited by 4 publications

(1 citation statement)

References 24 publications

(26 reference statements)

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“…In a related development, a method that relied on the information collected by shoe-mounted accelerometers and gyroscopes, and on the spacing between the feet extracted by ultra-wideband (UWB) transceivers has been reportedly used to design a smart shoe user authentication system with 97% accuracy (Brombin et al , 2019).…”

Section: Applicationsmentioning

confidence: 99%

Review of development trends in smart shoe applications

Raji,

Han,

et al. 2024

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Purpose At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications. Design/methodology/approach Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications. Findings This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development. Originality/value To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

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Section: Applicationsmentioning

confidence: 99%

Review of development trends in smart shoe applications

Raji,

Han,

et al. 2024

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Adaptive Distance Sensing in Contact Tracing Applications Through Indoor/Outdoor Detection

Essaid

Lorenzoni

Scatena

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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User gait biometrics in smart ambient applications through wearable accelerometer signals: an analysis of the influence of training setup on recognition accuracy

De Marsico,

Palermo

2024

J Ambient Intell Human Comput

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Gait recognition can exploit the signals from wearables, e.g., the accelerometers embedded in smart devices. At present, this kind of recognition mostly underlies subject verification: the incoming probe is compared only with the templates in the system gallery that belong to the claimed identity. For instance, several proposals tackle the continuous recognition of the device owner to detect possible theft or loss. In this case, assuming a short time between the gallery template acquisition and the probe is reasonable. This work rather investigates the viability of a wider range of applications including identification (comparison with a whole system gallery) in the medium-long term. The first contribution is a procedure for extraction and two-phase selection of the most relevant aggregate features from a gait signal. A model is trained for each identity using Logistic Regression. The second contribution is the experiments investigating the effect of the variability of the gait pattern in time. In particular, the recognition performance is influenced by the benchmark partition into training and testing sets when more acquisition sessions are available, like in the exploited ZJU-gaitacc dataset. When close-in-time acquisition data is only available, the results seem to suggest re-identification (short time among captures) as the most promising application for this kind of recognition. The exclusive use of different dataset sessions for training and testing can rather better highlight the dramatic effect of trait variability on the measured performance. This suggests acquiring enrollment data in more sessions when the intended use is in medium-long term applications of smart ambient intelligence.

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User’s Authentication Using Information Collected by Smart-Shoes

Cited by 4 publications

References 24 publications

Review of development trends in smart shoe applications

Review of development trends in smart shoe applications

Adaptive Distance Sensing in Contact Tracing Applications Through Indoor/Outdoor Detection

User gait biometrics in smart ambient applications through wearable accelerometer signals: an analysis of the influence of training setup on recognition accuracy

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