Nowadays, We live in a digital age where novel technological improvements in processing units and sensors have resulted in the development of smart wearable devices. Indeed, the several merits of wearable devices have motivated different studies for using wearable devices for human recognition. Recently, several behavioural patterns have been captured through wearable devices for human recognition. However, most related work have used a small set of activities in their studies. Unlike the previous work, we have considered a comprehensive dataset of twenty daily activities for subject recognition. Our previous publication has exploited the behavioural patterns data of known activities captured from wearable devices for subject recognition. In this paper, the main objective is to investigate the recent idea of activity-independent subject identification. The study found that the recognition accuracy results significantly vary across all the subjects. Also, the activities with a larger movement of more than one body position showed promising recognition accuracy results in distinguishing subjects compared to activities with few body motions such as talking phone, texting phone, and watching TV.