Walking Through the Deep: Gait Analysis for User Authentication Through Deep Learning

This manuscript presents an approach to the challenge of biometric identification based on the acceleration patterns generated by a user while walking. The proposed approach uses the data captured by a smartphone’s accelerometer and gyroscope sensors while the users perform the gait activity and optimizes the design of a recurrent neural network (RNN) to optimally learn the features that better characterize each individual. The database is composed of 15 users, and the acceleration data provided has a tri-axial format in the X-Y-Z axes. Data are pre-processed to estimate the vertical acceleration (in the direction of the gravity force). A deep recurrent neural network model consisting of LSTM cells divided into several layers and dense output layers is used for user recognition. The precision results obtained by the final architecture are above 97% in most executions. The proposed deep neural network-based architecture is tested in different scenarios to check its efficiency and robustness.

Section: Discussionsupporting

confidence: 66%

Section: State-of-the-artmentioning

confidence: 99%

Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns

Peinado-Contreras

Muñoz-Organero

2020

“…More recently, some studies propose the combination of micro-Doppler data and deep learning algorithms for the gait-based human recognition [ 13 , 14 , 15 , 16 ]. However, the hierarchical structure of deep learning is more suitable to identify complicated patterns from raw data (i.e., images and signals) without any feature extraction [ 24 , 25 ]. According to this, in [ 13 ], a deep autoencoder is used to perform human gait recognition with micro-Doppler radar.…”

Section: Related Workmentioning

confidence: 99%

Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition

Addabbo

Bernardi

Biondi

et al. 2021

The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in order to perform human identification. Moreover, they are strongly influenced by external factors (e.g., light and weather). This paper proposes an approach based on a temporal convolutional deep neural networks classifier applied to radar micro-Doppler signatures in order to identify individuals. Both sensor and processing requirements ensure a low size weight and power profile, enabling large scale deployment of discrete human identification systems. The proposed approach is assessed on real data concerning 106 individuals. The results show good accuracy of the classifier (the best obtained accuracy is 0.89 with an F1-score of 0.885) and improved performance when compared to other standard approaches.

“…Other AI techniques have been applied to different measurements. For example, artificial neural networks (ANN) for stride length estimation in [8], machine learning classifiers, such as k-Nearest Neighbor (k-NN), SVM, Naive Bayes (NB), and decision trees (DTs) to study the gait behavior of Parkinson's patients in [9], a Recurrent Convolutional Neural Network (RCNN) model for gait-based user authentication introduced by [10], Long-Short Term Memory (LSTM) algorithm for normal and pathological gait classification presented by [11] and SVM for gait classification of young and elderly people proposed by [12].…”

Section: Introductionmentioning

confidence: 99%

Gait Phase Estimation by Using LSTM in IMU-Based Gait Analysis—Proof of Concept

Sarshar

Polturi

Schega

2021

Gait phase detection in IMU-based gait analysis has some limitations due to walking style variations and physical impairments of individuals. Therefore, available algorithms may not work properly when the gait data is noisy, or the person rarely reaches a steady state of walking. The aim of this work was to employ Artificial Intelligence (AI), specifically a long short-term memory (LSTM) algorithm, to overcome these weaknesses. Three supervised LSTM-based models were designed to estimate the expected gait phases, including foot-off (FO), mid-swing (MidS) and foot-contact (FC). For collecting gait data two tri-axial inertial sensors were located above each ankle. The angular velocity magnitude, rotation matrix magnitude and free acceleration magnitude were captured for data labeling and turning detection and to strengthen the model, respectively. To do so, a train dataset based on a novel movement protocol was acquired. A validation dataset similar to a train dataset was generated as well. Five test datasets from already existing data were also created to independently evaluate the models. After testing the models on validation and test datasets, all three models demonstrated promising performance in estimating desired gait phases. The proposed approach proves the possibility of employing AI-based algorithms to predict labeled gait phases from a time series of gait data.