Identifying users of portable devices from gait pattern with accelerometers

By Jani Mäntyjärvi, Mikko Lindholm, Elena Vildjiounaite, Satu-Marja Mäkelä, H. Ailisto. Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005

453 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/016294079ee5fa35ec867cf2f658d9eb8cec4a4a

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Citations in the corpus

Inertial Sensor-Based Gait Recognition: A Review (2015, methodology): “Concerning parameters in frequency domain, existing approaches leverage FFT coefﬁcients [24,36,56,72,76,78], wavelet transform [65,67], cepstral coefﬁcients [37,50,70,73] and discrete cosine transform [36,50,56,78].”
uWave: Accelerometer-based personalized gesture recognition and its applications (2009, methodology): “Related to our use of acceleration, the work in [25] employed accelerometers to recognize the user with the gait pattern as a behavioral biometrics.”
Zero Trust Architecture (ZTA): A Comprehensive Survey (2022, methodology): “…use the raw data corresponding to a gait pattern (and then use correlation or machine learning for performing continuous authentication, e.g. [35] [36]), or they extract features (e.g. fast Fourier transform or Wavelet coefﬁcients) from the captured data to train the classiﬁer and perform…”
Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges (2016, methodology): “For instance, methods based on correlation, frequency domain analysis, and data distribution statics are used in [32], while methods based on dynamic time warping are used in [36] and [37].”
Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition (2010, background): “Early studies from psychology [4], medicine [5] and biometrics [6], [7] already give evidence that human gait contains very distinctive patterns that can be used for identification and verification purposes.”
Biometric Gait Authentication Using Accelerometer Sensor (2006, methodology): “[8].”
Spearphone: A Speech Privacy Exploit via Accelerometer-Sensed Reverberations from Smartphone Loudspeakers (2019, background): “Recent research [1], [2], [3], [4], [5], [6] exploits motion sensors for eavesdropping on keystrokes, touch input and speech.”
Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living (2019, background): “Several other studies, all conducted before 2009, similarly attached sensors to body position(s) to perform gait biometrics [22]–[24].”
Continuous mobile authentication using touchscreen gestures (2012, methodology): “[17] used accelerometers in television remote controls to identify individuals.”
Gyrophone: Recognizing Speech from Gyroscope Signals (2014, methodology): “[34] the authors suggest a method for user identification from gait patterns obtained from a mobile device’s accelerometers.”
Cell phone-based biometric identification (2010, background): “[6], where users were identified via a small accelerometer-based device placed on a belt at the middle of the user’s waistlines in back.”
Touch me once and i know it's you!: implicit authentication based on touch screen patterns (2012, methodology): “In [23] acceleration signals are used for user identification, whereas [4,7] used typing patterns to authenticate users on mobile devices using static classifiers and neural network classifiers, respectively.”