## 2 Related Work  
Researchers have made significant progress in human activity recognition by using wearable accelerometer. Another issue in most current literature is that transitions between multiple activitiesare usually ignored due to their short duration. If this issue is not addressed properly, these transitions would affect the final performance of activity recognition system since they would be misclassified as the available classes. [PERSON] et al. [8] proposed a transition-aware human activity recognition system for the recognition of six physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying) and six transitional activities (stand-to-sit, sit-to-stand, sit-to-lie, lie-to-sit, stand-to-lie and lie-to-stand) using the accelerometer embedded in a smartphone. The result showed the support vector machine could achieve high classification accuracy with heuristic filtering approach. [PERSON] et al. [9] proposed an offline unobtrusive system for monitoring basic activities as well as a joint class of posture transitions. A novel hierarchical classification that combines rule-based reasoning with support vector machine was adopted rather than using a single classifier. Due to the very short duration of transitional activities compared to static activities and dynamic activities, the proper selection of segmentation techniques has great potential impact on the success of feature extraction and classification algorithms, directly resulting in the accuracy of activity recognition. The sliding window-based segmentation method is probably the most common used segmentation method for activity recognition. It adopts either fixed window size or dynamic window size to divide the continuous sensor data stream into segments. To determine the most appropriate sliding window size, [PERSON] et al. [10] conducted an experiment to compare the activity classification performance of different sliding window segmentation methods with diverse window overlap values varying from 0% to 90%. Their study illustrated that the 90% overlapping sliding window segmentation with window size of 12 s contributed the optimal activity recognition accuracy of 98.38%. Dynamic sliding windows segmentation method employs differing window sizes on activity duration based on different sensor features. [PERSON] et al. [11] presented a novel approach for dynamic sliding window technique applied for recognition of static activities, dynamic activities as well as transitional activities. The proposed approach specified a small initial window size which enables to segment dynamic and static activity signals, and then expanded the window size to accommodate signal which is longer than the initial window size. [PERSON] et al. [12] combined the static and dynamic sliding window sizes and improved the sliding window segmentation method by obtaining the adaptive size of the window depending on the environment, the sensor data, and the monitored activity, to cope with the sensor events within one window and between adjacent windows. Generally, the sliding window-based segmentation method has the advantage of low computation complexity so that is suitable for continuous sensor data stream over a long period of time. However, the determination of the optimal value for the window size remains a critical issue. Too small window size may divide one activity into two adjacent windows and result in the loss of effective information, especially for the activity with long duration, such as cooking. Too wide window size may combine two or more activities into one window and introduce extra noise for accurate activity classification. The novelty of this paper lies in dynamically detecting change points in data stream and subsequently using these change points as window start position and end position. Data-driven approaches have been widely used in accelerometer data analysis for activity recognition.