An Architecture for Gesture-Based Control of Mobile Robots (Iba 1999)

Summary:
The paper looks at the use of hand gestures as a way for inexperienced users to direct robots. A difficulty arises in communicating the intention of a task to a robot instead of providing a set of actions that can be mimicked. The system described in the paper interprets gestures as either local or global instructions to a robot. Raw data captured from a CyberGlove is compacted into a 20-element vector describing 10 features and their first derivatives. The 20 dimensional data is reduced to one of 32 codewords. A 3-state HMM leverages temporal data from the gestures, turning sequences of codewords into one of six defined gestures, or an unspecified gesture. The HMM allows the classification of non-gestures (those whose probability is less than 0.5) due to the addition of an initial wait state in the HMM. The paper reports 96% accuracy in recognizing gestures using the HMM with the wait state.

Discussion:
The key insight in this paper's technique is the introduction of a "wait state" in the HMM that allows a gesture to be classified as not recognized. Adding more gestures to be recognized would reduce the effectiveness of a wait state, so the technique may not be well suited for distinguishing individual gestures from a large collection.
The distinction between gesture recognition and gesture spotting made at the beginning of the paper is important. The exclusion of non-gestures from an input sequence is a crucial facet of gesture spotting that distinguishes it from gesture recognition. This paper helped me form a better context for the possible applications of HMMs to gesture recognition. It was a good choice so soon after the Introduction to HMM paper by Rabiner and Juang.
The paper mentions that the length of an observation sequence should be limited to maintain a high probability that the observation sequence is produced by the model. However, no guidelines are given as how to select n, the number of most recent observations in a sequence.
It was not clear whether the waving motion to dodge obstacles temporarily placed the robot on a detour in the direction of the wave until the obstacle was passed, or permanently altered the destination of the robot. I suspect the latter was implemented, since it would be easier to divert the robot along a straight trajectory in th direction of the wave. Performing some kind of obstacle avoidance for a temporary detour could be a possible improvement for the system.