The impact of motion dimensionality and bit cardinality on the design of 3D gesture recognizers

The interactive demands of the upcoming ubiquitous computing era have set off researchers and practitioners toward prototyping new gesture-sensing devices and gadgets. At the same time, the practical needs of developing for such miniaturized prototypes with sometimes very low processing power and memory resources make practitioners in high demand of fast gesture recognizers employing little memory. However, the available work on motion gesture classifiers has mainly focused on delivering high recognition performance with less discussion on execution speed or required memory. This work investigates the performance of today's commonly used 3D motion gesture recognizers under the effect of different gesture dimensionality and bit cardinality representations. Specifically, we show that few sampling points and low bit depths are sufficient for most motion gesture metrics to attain their peak recognition performance in the context of the popular Nearest-Neighbor classification approach. As a practical consequence, 16x faster recognizers working with 32x less memory while delivering the same high levels of recognition performance are being reported. We present recognition results for a large gesture corpus consisting in nearly 20,000 gesture samples. In addition, a toolkit is provided to assist practitioners in optimizing their gesture recognizers in order to increase classification speed and reduce memory consumption for their designs. At a deeper level, our findings suggest that the precision of the human motor control system articulating 3D gestures is needlessly surpassed by the precision of today's motion sensing technology that unfortunately bares a direct connection with the sensors' cost. We hope this work will encourage practitioners to consider improving the performance of their prototypes by careful analysis of motion gesture representation rather than by throwing more processing power and more memory into the design. (C) 2012 Elsevier Ltd. All rights reserved.