## 3 Literature Review
### Behavioral Biometrics
#### 3.1.4 Gait Dynamics
DL algorithms achieved a better imposter detection than the ML algorithms in both scenarios with regard to a balance of execution time, memory usage, and accuracy. LSTM with prediction error distributions was able to achieve an accuracy of 90.24% in the LAD scenario with a 200 reading window. For [68], researchers proposed ContAuth, a continuous mobile authentication tested on gait, breathing, and electromyography (EMG) data. The architecture for this model used an LSTM for feature extraction and a stochastic gradient descendent (SGD) algorithm for classification. Incremental learning algorithms were also employed when encountering a new class. To train their model, four small-scale breathing datasets, an EMG dataset, and the IDNet [63] gait dataset were used in separate evaluations. With incremental learning and gait data, ContAuth was able to achieve an authentication accuracy of 97%, the highest between the dynamics tested. The performance of ContAuth is compared to other reviewed studies using the IDNet dataset in Table 3.Hybrid classification models using LSTM and CNN are the most popular choice for gait authentication schemes [69, 70, 71, 72, 73]. Researchers of [69] evaluatedthe performance of mobile gait recognition in unconstrained conditions. Their hybrid DL architecture included a CNN for feature extraction and a two-layer LSTM for prediction. Accelerometer and gyroscope mobile sensor data from the WhuGAIT datasets were used to train and evaluate their model. This collection of datasets consists of mobile gait data collected from 118 participants in the wild. For evaluation, multiple combinations of DL methods were compared, and LSTM was evaluated with three different architectures: one layered, bidirectional, and two layered. Results from this study found that the accelerometer sensor better captured gait features when compared with gyroscope, but that they were complementary overall. LSTM and CNN were found to be effective with extracting inertial time series gait data. The hybrid CNN + LSTM model also outperformed standalone CNN and LSTM models, with a 93.75% authentication accuracy with vertically aligned samples. In study [70], an implicit gait mobile authentication model using edge computing was proposed. Their scheme involves generating their authentication model on a cloud server and deploying it on an edge device (mobile device) to preserve efficiency and computing resources. Their authentication model involves a CNN for feature extraction and an LSTM for classification, and the model was trained and tested using gait data from the IDNet dataset [63]. A visual diagram of the gait cycle is demonstrated in Figure 6. SVM and CNN were used as comparison baselines for model evaluation. The CNN-LSTM hybrid model outperformed with an accuracy of 97.7% and did not experience a significant dip in performance when less training data were incorporated. For [71], a cross-modal mobile authentication system via DL was evaluated. Their model consisted of a time-distributed cross-modal architecture with CNN feature extraction and LSTM interpretation. To train their model, researchers in this study used a fifty-person dataset consisting of walking data captured from accelerometer and gyroscope sensors. Their cross-modal CNN-LSTM model outperformed comparison algorithms (K-neighbors, DT, RF, CNN + PCA + OC-SVM) with a 92.3% accuracy. GaitPrivacyOn, a gait mobile authentication scheme, was proposed in [72]. This model utilized an architecture with two modules. In the first, an autoencoder is used to transform raw gait data to preserve user privacy. In the second module, a Siamese CNN-RNN is utilized for the model's verification system consisting of three convolutional layers and a bidirectional LSTM layer. Two public datasets consisting of motion sensor data from gait activity were used to train their model. Results found that this model was able to differentiate activity with 99.2% AUC and authenticate users with AUC ranging from 91.5% to 99.9%.