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With the challenge of healthcare for the increasing number of elderly people and the prevalence of chronic disease, research has been carried out on the development of assistive technologies and devices. This paper proposes a framework of context-aware physiological analysis for remote and efficient healthcare. With the relationship between the physiological function and daily activities, the online detection of abnormal situation needs to be carried out given such rich context information. Two core modules in the framework are discussed in details by proposing hierarchical online activity recognition and dynamic Cumulative Sum Control Chart (CUSUM) methods for process control. Corresponding experiments have been set up to collect both ECG data and upper-body accelerations from two healthy participants. This framework also has great potential to be used for long term health drift detection by comparison of the physiological function patterns given the activity across different periods of time.