## Proposed User Verification Model  
This work utilises the recently published WISDM dataset ([[https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+](https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+)]([https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+](https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+))) (publicly available) and its experimental results based on four sensors (accelerometers and gyro sensors from both smartphone and smartwatch) and 18 activities (Table 1) to develop a mechanism to implement continuous and adaptive user verification using behavioural biometrics as a part of multi-factor authentication [2]. [PERSON] et al. in 2019 described how simple machine learning models achieve high rates of accuracy on the task of identifying and authenticating users based on their motion biometrics using activities of daily life. They performed the experiment on the WISDM data ([[https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+](https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+)]([https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+](https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+))) where they categorised the recorded activities into three classes, namely: non-hand oriented activities (for example walking, jogging, etc. ); general hand-oriented activities (for example clapping, typing, etc. ); and hand-oriented eating activities (for example eating a sandwich, pasta, etc.) [2; 23]. Since the WISDM dataset ([[https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+](https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+)]([https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+](https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+))) contains data from both the smartphone and smartwatch, they conducted the experiment on various combinations of four sensors (phone accelerometer, phone gyrosensor, watch accelerometer and watch gyrosensor). For both the tasks, i.e., user identification and authentication, they trained a separate machine learn \begin{table}
\begin{tabular}{c c} \hline \hline  & **Non-hand oriented activities** \\ \hline A & walking \\ B & jogging \\ C & stairs \\ D & sitting \\ E & standing \\ M & kicking a ball \\ \hline \multicolumn{3}{c}{**Hand-oriented activities**} \\ \hline F & typing \\ G & teeth \\ O & catch \\ P & dribbling \\ Q & writing \\ R & clapping \\ S & folding \\ \hline \multicolumn{3}{c}{**Hand-oriented eating activities**} \\ \hline H & soup \\ I & chips \\ J & pasta \\ K & drinking \\ L & sandwich \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The physical activities. ing model for each activity. The analysis tested three different machine learning models, namely Random Forest, Decision Tree and K-nearest neighbours, out of which Random Forest performed the best for all activities and every sensor combination. This was one of the first studies focusing on daily life activities for the purpose of user identification or authentication. This work plans to take up the analysis by [PERSON] et al. and employ the results to build a user verification system that is ready to be integrated and utilised as a part of the current day user authentication systems. As the purpose of this algorithm is to authenticate users, it is essential that the underlying machine learning models are tuned to achieve better accuracy.