Heart Rate Variability (HRV) is proven to be related to critical cardiovascular diseases. However, HRV is related to other factors as well, among which the activity is the major one. A novel activity-aware HRV analysis method is proposed to improve its performance on daily life ECG data. First, activity types and intensity are obtained by processing 3-axis acceleration signals. Then heart rate data extracted from ECG signal are segmented according to the activity types and intensity. The heart rate segments are evaluated to determine its suitability for HRV analysis. The suitable segments are further processed to remove outliers and slow non-stationary trends caused by activity instability and other factors. Finally, HRV analysis is conducted on these suitable and processed segments, and the results are labeled with activity context information. Thus, HRV s under different activity contexts are obtained. The advantages of activity-aware HRV are: First, affection of activity on HRV stability is reduced. Second, we have gained HRV dynamics measure against activity types and intensity.