1 research outputs found
GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments
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