Automatic tracking of intra-beat cardiac activities in ballistocardiogram (BCG) is a highly interesting yet technically challenging topic for cardiac monitoring, due to the signal's high susceptibility to various forms of distortions. In this paper, we aim to further investigate the BCG waveform detection from a signal processing and analysis viewpoint. We collect synchronized electrocardiography(ECG) and BCG recordings from four healthy human subjects using an in-house built multi-physiological monitoring device. Particularly, we study post-exercise ECG-BCG signals that embed considerable variation in the heart beat during the post-exercise recovery phase. Furthermore, we develop an efficient and interactive tool for detecting and marking ECG-BCG waveforms in each heart beat. Through analyzing the detected time interval signals, we explore new interesting patterns of dynamic associations between different time interval signals. At the same time, we call for development of improved detection algorithms to address robustness and accuracy issues.