DOI: 10.5061/dryad.dfn2z34xs
title: Data from: Enhancing the security of pattern unlock with surface EMG-based biometrics

description: Pattern unlock is a popular screen unlock scheme that protects the
 sensitive data and information stored in mobile devices from unauthorized
 access. However, it is also susceptible to various attacks, including
 guessing attacks, shoulder surfing attacks, smudge attacks, and
 side-channel attacks, which can achieve a high success rate in breaking
 the patterns. In this paper, we propose a new two-factor screen unlock
 scheme that incorporates surface electromyography (sEMG)-based biometrics
 with patterns for user authentication. sEMG signals are unique biometric
 traits suitable for person identification, which can greatly improve the
 security of pattern unlock. During a screen unlock session, sEMG signals
 are recorded when the user draws the pattern on the device screen.
 Time-domain features extracted from the recorded sEMG signals are then
 used as the input of a one-class classifier to identify the user is
 legitimate or not. We conducted an experiment involving 10 subjects to
 test the effectiveness of the proposed scheme. It is shown that the
 adopted time-domain sEMG features and one-class classifiers achieve good
 authentication performance in terms of the F 1 score and Half of Total
 Error Rate (HTER). The results demonstrate that the proposed scheme is a
 promising solution to enhance the security of pattern unlock.
description: Subjects: The data collecting process was approved by the
 Institutional Review Board (IRB) of the New Mexico Institute of Mining and
 Technology. We recruited 10 subjects from the school (7 males and 3
 females, age = 23.8 ± 2.5 years, height = 173.9 ± 9.7 cm, weight = 70.0 ±
 15.6 kg, all right-handed) to participate at the data collecting process.
 All participants reported that they did not have upper limb
 musculoskeletal and nervous system diseases.
 Acquisition: The OpenBCI cyton board was used to collect the sEMG
 generated by using the right-hand index finger to draw the unlock pattern
 on an Android mobile phone. The Cyton biosensing board (OpenBCI) recorded
 finger movement through a gold cup electrode (OpenBCI) placed on the FDS
 muscle of each subject’s forearm. The sampling rate of the Cyton board is
 250 Hz. The acquired sEMG signal is then sent from the Cyton board to the
 Cyton Dongle (OpenBCI), a Bluetooth adaptor plugged into a laptop. The
 OpenBCI GUI software installed on the laptop then records the acquired
 data in the local storage. Each subject was instructed
 to draw two patterns. Each pattern was drawn repeatedly for 20 trials.
 sEMG signals were recorded for each trial. The subject was instructed to
 rest the arm on the table for one minute before the next trial to avoid
 muscle fatigue. The whole process for a subject lasted about one hour and
 twenty minutes, which consisted of 40 trials for the two
 patterns. Pre-processing: The quality of the recorded
 raw sEMG signals was affected by noises, such as direct current offset,
 environmental noises, and artifact noises. We applied a 5Hz high-pass
 filter to eliminate the direct current offset, baseline drift due to the
 movement in recording, and perspiration. A 60Hz notch filter was then
 applied to filter out the power-line noise.
description: The dataset contains 400 data files corresponding to the 400 trials of the 10 subjects. Each data file is a 6s sEMG signal segment extracted after the pre-processing starting from the time when the subject started the drawing of the unlock pattern. Therefore, each data file consists of 1500 data points. In our study, time-domain features were extracted from the signal segment for authentication purpose. The data files are organized in 10 directories corresponding to the 10 subjects. In each directory, the file is named as<b> pp_tt.csv</b>, where<b> pp</b> is the pattern number, and <b>tt </b>is the trial ID. For example, in directory 01, p1_01.csv contains the sEMG signal segment from subject 1 drawing pattern 1 in the first trial. For publication purposes, the data for each subject have been compressed into zip files. Zipped filenames follow the patter: <em>li_dong_zheng_2020_pattern_unlock_subject_NN_sEMG.zip</em> where <em>NN </em>is given as the subject's number (01-10).