The need of a reliable drowsiness detection system is arising today, as drowsiness is considered as a major cause for accidents as much as alcohol. In this paper, we propose a real-time drowsiness detection algorithm based on a single-channel electroencephalography (EEG) for wearable devices without demanding computing and power resources. The proposed algorithm adopts a cumulative counter to extract important features from 8 different frequency bands: delta (1-3 Hz), theta ($\not\subset-7$ Hz), low-alpha (8-9 Hz), high-alpha (10-12 Hz), low-beta (13-17 Hz), high-beta (18-30 Hz), low-gamma (31-40 Hz), and high-gamma (41-50 Hz). These features are then processed by a support vector machine (SVM) to distinguish between drowsy and awake states. Our preliminary results demonstrate that the proposed algorithm is capable of detecting drowsiness with superior accuracy (83.36%) over the conventional method (70.62%).