Tuesday, January 11, 2011

Bayesian Networks and Gesture-Based Human-Machine Interfaces

The 2010 Horizon Report highlights gesture-based computing as an upcoming technology with a five-year horizon.  The main goal of gesture-based computing is to provide an untethered human-machine interface (HMI) capable of capturing  and interpreting the myriad of human motion and emotion. Current gesture-recognition systems typically employ  a single
“camera” or sensor comprised of a CMOS IC (Complementary metal–oxide–semiconductor Integrated Circuit) to detect motion, such as a pointing finger or a facial gesture, such as a smile. The size (in megabytes) of readily available CMOS sensors  provides a data stream sufficient for numerous commercial uses and can be found in electronic games, automobiles, and  industrial applications.

While CMOS manufacturing technology has advanced sufficiently to make widespread CMOS sensor utilization economically feasible,  interpreting the motion or gestures they capture requires advanced cognitive abilities to infer their meaning and anticipate future situations. Just as we have learned the meaning of a goodbye wave and to anticipate departure  of the gesturer, Bayesian probabilistic modeling or Bayesian Networks (BNs) provide a wide range of techniques ideally suited  to learning the states of a gesture, to infer their meaning, and to predict an outcome of the gesture. Unlike classic  probabilistic methodologies, BNs provide a predictive capability based on updated information. As a motion is initiated and  proceeds to completion, BN techniques are able to incorporate new information to exclude unlikely choices to arrive at the  meaning of motion or gesture.

1 comment:

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