Shenzhen University

Yandao Huang

Lin Chen

Kaishun Wu

Cong Li

Wenkai Yang

A method and a system for recognizing a two-dimensional (2D) movement track based on a smart watch is provided. The method comprises: acquiring accelerometer signal data and gyroscope signal data of the smart watch; estimating a tilt angle of the smart watch by using the accelerometer signal data and correcting the gyroscope signal data by using the tilt angle; and calculating angle value information of the smart watch by using the corrected gyroscope signal data and estimating a coordinate point. According to the present application, the movement track of the smart watch can be accurately estimated by using the accelerometer and the gyroscope built in the smart watch.

Method and system for recognizing 2D movement track based on smart watch

A vibration signal-based smartwatch authentication method includes generating incremental vibration signals using a vibration motor in a smartwatch; performing frequency band-based hierarchical endpoint segmentation to obtain vibration signals at a plurality of frequency bands; extracting frequency-domain features for the vibration signals at the plurality of frequency bands; training a dynamic time warping model by taking the vibration signals at the plurality of frequency bands as a training data set, training a nearest neighbor model by taking the extracted frequency-domain features as training data; collecting to-be-authenticated vibration signals which are processed to serve as test data signals; discriminating similarities between the test data signals and corresponding training data signals through the dynamic time warping model, giving a classification result through the nearest neighbor model, performing weighted calculation on a discrimination result of the dynamic time warping model and a discrimination result of the nearest neighbor model to obtain an authentication result.

Vibration signal-based smartwatch authentication method

A vibration-based authentication method for an access control system includes: collecting vibration signals generated by a built-in vibration motor in an authentication device; filtering, denoising, and performing endpoint segmentation on the collected vibration signals, and extracting vibration signals containing effective touch; performing an alignment on the segmented vibration signals; performing a fast Fourier transform on the aligned vibration signals to obtain frequency-domain data, extracting frequency-domain features obtained after alignment and features obtained before alignment to construct a training data set, and storing the training data set in a database of the authentication device; using a new unlock signal generated when a user touches the authentication device as test data, and processing the test data to obtain test data containing effective touch; and matching and classifying the test data containing effective touch with the training data set by using a machine learning classification model, to obtain an authentication result.

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Shenzhen, China

Yandao Huang