Force myography benchmark data for hand gesture recognition and transfer learning
This provides a standardized benchmark for researchers in hand gesture recognition, facilitating algorithm comparison and easier entry into the field, though it is incremental as it focuses on data availability rather than novel methods.
The authors addressed the lack of publicly available benchmark data for force myography in hand gesture recognition by releasing a dataset from 20 persons covering 18 gestures, collected with commercial sensors, and demonstrated its utility by improving gesture recognition accuracy using transfer learning across multiple persons.
Force myography has recently gained increasing attention for hand gesture recognition tasks. However, there is a lack of publicly available benchmark data, with most existing studies collecting their own data often with custom hardware and for varying sets of gestures. This limits the ability to compare various algorithms, as well as the possibility for research to be done without first needing to collect data oneself. We contribute to the advancement of this field by making accessible a benchmark dataset collected using a commercially available sensor setup from 20 persons covering 18 unique gestures, in the hope of allowing further comparison of results as well as easier entry into this field of research. We illustrate one use-case for such data, showing how we can improve gesture recognition accuracy by utilising transfer learning to incorporate data from multiple other persons. This also illustrates that the dataset can serve as a benchmark dataset to facilitate research on transfer learning algorithms.