Biometric Gait Authentication Using Accelerometer Sensor

By Davrondzhon Gafurov, Kirsi Helkala, Torkjel Søndrol. Journal of Computers, 2006

337 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/456f85fb61fa5f137431e6d12c5fc73cc2ebaced

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

Inertial Sensor-Based Gait Recognition: A Review (2015, methodology): “Approaches that rely on parameters in time domain apply analysis of local extrema [34,36,56,74,78], zero-crossing [55], cycle length [25], gait cycle frequency [36,53,56,78], gait symmetry [53] or dynamic range analysis [53,71].”
WiWho: WiFi-Based Person Identification in Smart Spaces (2016, background): “Other works leverage various sensors, such as floor sensors [17], rotation sensors [18] and accelerometerbased wearable or smartphone sensors [19]–[21], to capture gait signature.”
Touch me once and i know it's you!: implicit authentication based on touch screen patterns (2012, background): “Exemplary cues are the user’s gait [10,16], location information [14] or keystroke patterns [5,22].”
Continuous mobile authentication using touchscreen gestures (2012, methodology): “[11] experimented user identification using gait analysis and recognition.”
Cell phone-based biometric identification (2010, methodology): “Gafurov, Helkala, & Sondrol [8] authenticated user’s identities using data collected from 21 users, using a tri-axial accelerometer-based device attached to the user’s right lower legs.”
Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living (2019, methodology): “In this study, authentication performance is evaluated using Equal Error Rate (EER), a common metric for com-paring authentication models [23].”
Implicit Authentication through Learning User Behavior (2010, background): “Kale et al. [13] and Gafurov et al. [9] used gait recognition to detect whether a device is being used by the owner.”
Person Identification Using Full-Body Motion and Anthropometric Biometrics from Kinect Videos (2012, background): “below the hips) gait biometric have been proposed that attempt to identify people with similar lower extremity gait kinematics [9–11], stride and cadence [12, 13], and mechanics [14, 15].”