Simultaneous Gesture Segmentation and Recognition based on Forward Spotting Accumulative HMMs (Song 2006)

Summary:
Most gesture segmentation and recognition systems examine gesture patterns for places to segment the data, which must be done before recognition. This requires that the gesture be complete before recognition begins, which delays the rate at which recognition can be performed. This work attempts to use a forward spotting scheme to perform gesture segmentation and recognition at the same time. A sliding window of observations is used to compute the probability of the observations within the window being a gesture or a non-gesture. The window averages affects of abruptly changing features. Accumulative HMMs are used in the gesture recognition portion of the system; they accept partial posture segments for which the most likely posture is determined. The final posture is determined by majority voting between the partial posture candidates.
An experiment using eight gestures to open and close curtains and turn lights on and off was performed to compare automatic and manual gesture segmentation and recognition. For segmentation, the automatic threshold method was reported to be 17.62% better than the manual threshold method. For recognition, the automatic method was reported to be 3.75% more accurate than the manual method.

Discussion:
The application of controlling curtains and lights in a home seemed a bit non-useful to me, since a gesture recognition system in the living room would probably receive a lot of signals that were not intended to be interpreted by the system. Installing such a system in a real home could cause some interesting scenarios for a family playing charades.
The results of the experiment depend on the manual recognition process, which was not described in the paper. Since there was no description of how the manual processes were performed, I don't know how to interpret the reported benefits of the automatic methods.
The gestures chosen for recognition are very simple and could be recognized by simply detecting whether a hand of the person passed some threshold of the camera space. HMMs are too complex of a solution for the type of gestures in the experiment.