Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures

Data glove devices, apart from being widely used in industry and entertainment, can also serve as a means for communication with the environment. This is possible thanks to the advancement in electronic technology and machine learning algorithms. In this paper, the results of the study using a designed data glove equipped with 10 piezoelectric sensors are reported, and the designed glove is validated on a recognition task of hand gestures based on 16 static signs of the Polish Sign Language (PSL) alphabet. The main result of the study is that recognition of 16 PSL static gestures is possible with a reduced number of piezoelectric sensors. This result has been achieved by applying the decision tree classifier that can rank the importance of the sensors for the recognition performance. Other machine learning algorithms were also tested, and it was showed that for the Support Vector Machines, k-NN and Bagged Trees classifiers, a recognition rate of the signs exceeding 90% can be achieved just for three preselected sensors. Such a result is important for a reduction in design complexity and costs of such a data glove with sustained reliability of the device.

Automated summary

This study reports the recognition of Polish Sign Language static gestures using a designed data glove equipped with a reduced number of piezoelectric sensors. The decision tree classifier was applied to rank the importance of sensors, achieving a recognition rate exceeding 90% with just three preselected sensors. This result is significant for reducing design complexity and costs while maintaining reliable performance of the device.