The hand-crafted (time and frequency-based) features that are extracted and selected are heuristic and rely on expert knowledge of the domain. The features may be effective in certain specific settings, but the same features might fail to discriminate the activities in a more general environment. Furthermore, hand-crafted feature extraction and selection are time-consuming, laborious and prone to error, and might still achieve suboptimal recognition performance. In this research, an unsupervised feature learning method for activity recognition is proposed. The proposed method eliminates the need for manual feature engineering, making it more accurate in learning the underlying features of the data. Furthermore, the proposed method maps the sensor data into a lower dimensionality feature space, consequently, reduces the computational cost and improves generalization. Interested readers are referred to  for more details.