# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 5 Experiment Design and Results Analysis  
We have used a few publicly available datasets to experiment with the proposed authentication algorithm detailed in this section. We have implemented our model using MATLAB. The design and implementation are done in a few steps: data acquisition and pre-process, feature extraction and selection, and finally, classification. The workflow diagram of the authentication ML model is presented in Figure 9.  
The notable elements of the workflow are as follows:  
1. **Physiological Signals:** The signals are classified as authenticated and non-authenticated signals. As the name suggests, authenticated signals refer to signals registered into the database that are collected from a trusted source. The non-authenticated signals attribute to the signals from an unknown source/user that needs identification/authentication;
2. **Data Preparation:** As the physiological data being obtained are mostly raw sensor data, data pre-processing is required. Some of the techniques that are used in this stage are band-pass and noise filtering to remove the noise and artefacts from the collected signal;
3. **Feature Engineering:** For single signal experiments, the features are directly extracted using the feature extraction algorithm. For multimodal experiments, we apply early fusion and then extract and select features from the fused signals;
4. **Identification:** The unknown signal is processed, and features are extracted from the signal in this stage. Then the authentication algorithm is run to check the authenticity against the stored patterns of the authenticated users.  
In this research work, we have used five datasets. All the signals in each dataset have been grouped in a single mat file. The signals are labelled as AUTH for authenticated users and NAUTH for non-authenticated signals.  
We use the work from [55] as a reference point to design our feature extraction part. We extract between 18 and 34 features for each experiment. Then the most important features are selected based on the chi-square test. All derived features are concatenated into a feature vector. The detail of our code is available in GitHub [56].  
The classification algorithms described previously are used to train and test the model. The parameters for each classification algorithm are selected based on the optimal performance. We define this performance benchmark by tuning several parameters and running each experiment at least five times. For example, when using the SVM algorithm, we have chosen the polynomial kernel function for SVM to classify each trial to estimate the misclassification rate and the confusion matrix. We selected the polynomial kernel function since our model is parametric. The polynomial function also works better if the model uses  
Figure 9: The workflow of creating ML-based authentication model.  
fewer data. Since we are training our model with authenticated user data, we do not expect a lot of data for this type of PH system.  
We have used multiple metrics to evaluate our model. Ten-fold cross-validation is used to estimate the misclassification rate and construct the confusion matrix. Then we derived the accuracy precision rate, recall rate, F1 score, model loss and Equal Error Rate (EER) for each of our experiments. Each of these metrics is calculated using True Positive (TP), False Positive (FP), True Negative (TN) and False Negative (FN). We have a range of specific settings for unimodal and multimodal signal experiments. The following subsections detail the experiment settings and results, including the dataset details.