This paper introduces and evaluates the GestureKeeper, a robust hand-gesture recognition system based on a wearable inertial measurements unit (IMU). The identification of the time windows where the gestures occur, without relying on an explicit user action or a special gesture marker, is a very challenging task. To address this problem, GestureKeeper identifies the start of a gesture by exploiting the underlying dynamics of the associated time series using a recurrence quantification analysis (RQA). RQA is a powerful method for nonlinear time-series analysis, which enables the detection of critical transitions in the system's dynamical behavior. Most importantly, it does not make any assumption about the underlying distribution or model that governs the data. Having estimated the gesture window, a support vector machine is employed to recognize the specific gesture. Our proposed method is evaluated by means of a small-scale pilot study at FORTH and demonstrated that GestureKeeper can identify correctly the start of a gesture with a 87\% mean balanced accuracy and classify correctly the specific hand-gesture with a mean accuracy of over 96\%. To the best of our knowledge, GestureKeeper is the first automatic hand-gesture identification system based only on accelerometer. The performance analysis reveals the predictive power of the features and the system's robustness in the presence of additive noise. We also performed a sensitivity analysis to examine the impact of various parameters and a comparative analysis of different classifiers (SVM, random forests). Most importantly, the system can be extended to incorporate a large dictionary of gestures and operate without further calibration for a new user.