# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 3 System Design  
This section elaborates on our designed physiological biometric-based device authentication system based on continuous authentication techniques. Figure 7 illustrates our system. The system uses two phases to verify a legitimate device--the enrolment phase and the constant authentication phase. The enrolment phase includes both user and device registration. The preliminary step of device registration is user registration. Figure 7: High-level Overview of the Proposed Authentication. At first, there is a range of biometric signals \(s_{i}\), where \(i=1,2,3,...,n\) signifies a series of biometric signals. These are collected from a valid user \(u_{i}\). The system is trained using a machine learning model to recognise legitimate users. The model has training and testing phases. In the training phase, the manager will use the right signal to make the model recognise the correct user. In the testing phase, the manager uses the reference dataset to test the success rate of the recognition. Algorithm 1 (User Registration) illustrates the pseudocode. User registration has been implemented in the experiment part using machine learning and deep learning algorithms,  
Let \(s_{i}\) be a set of signals from a user \(u_{i}\). In our case, \(s_{i}\) is a set of multiple signals such as \(ECG_{i}\) and \(PPG_{i}\). \[s_{i}=(ECG_{i},PPG_{i}). \tag{16}\]  
The signal is collected for a time period \(w_{i}\) from a user \(u_{i}\). A device \(d_{i}\) is trained with the signal for a time period \(trnw_{i}\). The device is tested with the signal for a time period \(tstw_{i}\). Then horizontal concatenation is performed on the signals to derive a single metric. ```
Input :An array \([s_{i}]\), \(i=1,2,\cdots,n\), where each element is a biometric signal Output :Registration Notification /* The device manager collects biometric signals from \([u_{i}]\) legitimate users */ whileInput Signals from \([u_{i}]\) user \(r\) number of records=true do  The manager trains the system with authenticated and non-authenticated signals; for\(r\)=0; \(r\)\(<=\)\(n\); \(r\)++ do  Feature Selection;  Divide into Train and Test data;  Train=TrainData;  Test=TestData;  End train the system;  Test;  end if ifTest successfulthen  register user \([u_{i}]\);  go to the next user;  end if  end for
```  
**Algorithm 1**User Registration. The system progresses to the device registration after the user registration phase. In this stage, the PH manager instructs the device to send biometric signals such as ECG and PPG for a specific time window from the attached user. The manager then uses the previously created model to authenticate and register the device in the system. Algorithm 2 describes conceptual the process in pseudocode format. ```
Input :An array \([s_{i}]\), \(i=1,2,\cdots,n\), where each element is a biometric signal Output :Device Registration Notification /* The device manager compares the device signal \([s_{i}]\), with the user signal \([u_{i}]\) */ whileInput Signals from \([d_{i}]\) matches the signal of \([u_{i}]\)do  register device \([d_{i}]\);  go to the next device;  end if
```  
**Algorithm 2**Device Registration. In the authentication phase, a device is asked to log in using the biometric signal of the attached user. If the model can recognise the signal, the device is authenticated. Otherwise, the system will request a higher privilege to reset the sensor or data to be passed or lock the device to prevent further access. The skeleton code for this authentication algorithm is described below. This authentication process is tested later in the experiment section. ```
/* A PH device sends data to manager */ ifValid Devicethen  Let the device send data; end else  Reject entry/ Lock / Ask for Higher Privilege; end
```  
**Algorithm 3**Authentication. On the other hand, continuous authentication makes sure that the device is constantly audited. This prevents session impersonation.