Transition-aware activity recognition is an inherent component of online health monitoring and ambient assisted living. An explosion of technology breakthroughs in wireless sensor networks, wearable computing, and mobile computing has facilitated this. However, real time, dynamic activity recognition is still challenging in practice. As reported in the existing literature, machine learning techniques are successfully used on the presegmented data to deliver transition-aware activity recognition systems. However, these strategies are frequently ineffective when used in a near-real-time context. This article presents an online change point detection (OCPD) strategy to segment the continuous multivariate time-series smartphone sensor data and its application in a transition-aware activity recognition framework. The proposed OCPD strategy is based on the hypothesis-and-verification principle. After the online data stream segmentation using the proposed OCPD strategy, feature engineering is performed to retain the essential features. Then, synthetic minority oversampling technique (SMOTE) is applied to balance the dataset. Finally, practical experiments are carried out to verify the suggested frameworks’ efficiency and reliability. The results reveal that the proposed OCPD strategy with ensemble classifier achieves a greater recognition rate (F-Measure: 99.80%) compared to methods stated in the literature.