  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.
