Fitness and activity tracking devices acquire, process, and store rich behavioural data that is consumed by the end-user to learn health insights. This rich data source also enables a secondary use of being part of a biometric authentication system. However, there are many open research challenges with the use of data generated by fitness and activity trackers as a biometric source. In this paper, the challenge of using data acquired from low-cost devices is tackled. This includes investigating how to best partition the data to deduce repeatable behavioural traits while maximising the uniqueness between participant data sets. In this exploratory research, three months' worth of data (heart rate, step count, and sleep) for five participants is acquired and utilised in its raw form from low-cost devices. It is established that dividing the data into 14 hour segments is deemed the most suitable based on measuring coefficients of variance. Several supervised machine learning algorithms are then applied where the performance is evaluated by six metrics to demonstrate the potential of employing this data source in biometric-based security systems.