  Most of industrial robots are still programmed using the typical teaching
process, through the use of the robot teach pendant. In this paper is proposed
an accelerometer-based system to control an industrial robot using two low-cost
and small 3-axis wireless accelerometers. These accelerometers are attached to
the human arms, capturing its behavior (gestures and postures). An Artificial
Neural Network (ANN) trained with a back-propagation algorithm was used to
recognize arm gestures and postures, which then will be used as input in the
control of the robot. The aim is that the robot starts the movement almost at
the same time as the user starts to perform a gesture or posture (low response
time). The results show that the system allows the control of an industrial
robot in an intuitive way. However, the achieved recognition rate of gestures
and postures (92%) should be improved in future, keeping the compromise with
the system response time (160 milliseconds). Finally, the results of some tests
performed with an industrial robot are presented and discussed.
