# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 2 Background and Related Works
For example, In [13], the authors studied the feasibility of using PPG data for authentication. They concluded that the results heavily depend on the quality of data. In [24], the authors combined both behavioural and physiological biometric features for user identification. Specifically, they employed Gait and PPG as the biometric trait. Their experimental results showed that Support Vector Machine (SVM) has a superior performance to KNN and Autoencoder Neural Network, although KNN achieved the fastest performance. Furthermore, they noticed that with sample size increment, the gap between KNN and Linear SVM accuracy becomes smaller. Besides the above studies, some research work considered multimodal authentication approaches. In [25], the authors implemented a multimodal biometric authentication method integrating face and iris based on score level fusion. The performance excels  
Figure 4: IoT Network Attacks. the performance of the unimodal biometric identification method and the previous fused face-iris methods. In [26], the authors adopted an SVM-based multimodal approach for identification. They applied a score level fusion approach, and k means clustering to classify a multi-SVM machine. A nonlinear classifier allows the SVM to perform a 'two-dimensional' classification of a set of originally one-dimensional data. In [27], a multimodal authentication model is proposed. The work applies a combination of ECG and fingerprint to authenticate the users and reports an EER of 0.1%. However, the ECG-only authentication method denotes a 90% accuracy. The authors in [28] designed a PPG-based nonfiducial biometric authentication method. They apply Continuous Wavelet Transform (CWT) and Direct Linear Discriminant Analysis (DLDA) and attained an EER of 0.5-6%. The study in [29] uses ECG and finger vein for multimodal authentication. The researchers report an EER of 0.12% and 1.40% with feature and score fusion. They apply Multi-Canonical Correlation Analysis (MCCA) with a range of classifiers, namely: Support Vector Machine (SVM), K-Nearest Neighbours (KNNs), Random Forest (RF), Naive Bayes (NB), and Artificial Neural Network (ANN). The authors of [30] propose two multimodal authentication systems, which are sequential and parallel, and use a combination of ECG and Fingerprint. They apply Convolution Neural Network (CNN) and Q-Gaussian multi-support vector machine (QG-MSVM). Using two datasets, they attained an EER of 0.14% and 0.10% for the sequential systems. The parallel system achieves an EER of 0.40% and 0.32% for respective datasets. The heuristics strategies to improve the resiliency of personalised healthcare services are studied in [31]. Table 1 presents the overview of similar works to our study. Many studies have focused on multimodal biometric authentication. However, only a few studies have comprehensively experimented with ECG and PPG signals. For example, the study [32] was one of the few to use both ECG and PPG signals to detect spoof detection and authentication. However, the method uses ECG and PPG signals separately. As a result, the processing time for this model is 85.31 ms with an accuracy of 98.9%. The other prominent work in this sense is the one reported in [28]. The authors denote an EER of 0.05 while combining ECG, PPG and GSR with 25 subjects. However, the EER and detailed results in the case of fused ECG and PPG have not been precisely reported. Additionally, the research did not report the in-depth study of the applied classification algorithms and feature extraction methods. Another recent work that has used ECG and PPG for biometric authentication is [33]. The study uses ECG and PPG signals separately for authentication. The MATLAB functions applied for feature extraction and classification are not explained explicitly. As a result, it is hard to reproduce the reported results. Compared to other studies, our work proposes a novel biometric-based continuous model that takes advantage of ECG and PPG fusion.