Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EarGate: Gait-based User Identification with In-ear Microphones   Human gait is a widely used biometric trait for user identification and
recognition. Given the wide-spreading, steady diffusion of ear-worn wearables
(Earables) as the new frontier of wearable devices, we investigate the
feasibility of earable-based gait identification. Specifically, we look at
gait-based identification from the sounds induced by walking and propagated
through the musculoskeletal system in the body. Our system, EarGate, leverages
an in-ear facing microphone which exploits the earable's occlusion effect to
reliably detect the user's gait from inside the ear canal, without impairing
the general usage of earphones. With data collected from 31 subjects, we show
that EarGate achieves up to 97.26% Balanced Accuracy (BAC) with very low False
Acceptance Rate (FAR) and False Rejection Rate (FRR) of 3.23% and 2.25%,
respectively. Further, our measurement of power consumption and latency
investigates how this gait identification model could live both as a
stand-alone or cloud-coupled earable system.

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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: eess
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org