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A portable data recorder was developed to parallel measure the electrocardiogram and body accelerations. A multilayer fuzzy clustering algorithm was proposed to classify the physical activity based on body accelerations. Discrete wavelet transform was incorporated to retrieve time-varying characteristics of heart rate variability under different physical activities. Nine healthy subjects were included to investigate activity-related heart rate variability during 24 h. The results showed that the heartbeat fluctuations in high frequencies were the greatest during lying and the smallest during standing. Moreover, very-low-frequency heartbeat fluctuations during low activity level (lying) were greater than during high activity level (nonlying).

Heartbeat detection is very important for retrieving the vital signs of heart functions. The morphologies and inter-beat intervals of heartbeats can reveal the condition of heart contraction. In this paper, we developed a heartbeat information integration scheme to deal with the information yielded by the energy thresholding and template match methods, which are usually used to detect the heartbeats and match the QRS, respectively. The proposed method are developed in SIMULINK 2.0 and assessed by the MIT/BIH arrhythmia database. The result demonstrated excellent sensitivity of detecting QRS and ventricular premature contraction in the proposed method.