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.
翻译:人类行踪是一种广泛使用的用户识别和识别生物鉴别特征。 鉴于耳盘穿戴器(Earables)作为可穿戴装置新前沿的宽广、稳步扩散,我们调查了可听取行装识别的可行性。 具体地说,我们从身体肌肉骨骼系统通过行走和传播的声响中查看以行踪为基础的识别特征。 我们的系统EarGate利用一个近距离对面的麦克风,利用可听听器的隔音效应从耳道内可靠地探测用户的行踪,同时不损害耳机的一般使用。 我们从31个学科收集的数据显示,EarGate达到97.26%的平衡行踪(BAC),假验收率和误验率分别为3.23%和2.25%。 此外,我们对功率消耗和延缓度的测量还调查了这一网格识别模型如何作为独立或云层可读取的可听系统运行。