Data from: A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

Upper limb robotic (myoelectric) prostheses are technologically advanced,
but challenging to use. In response, substantial research is being done to
develop person-specific prosthesis controllers that can predict a user’s
intended movements. Most studies that test and compare new controllers
rely on simple assessment measures such as task scores (e.g., number of
objects moved across a barrier) or duration-based measures (e.g., overall
task completion time). These assessment measures, however, fail to capture
valuable details about: the quality of device arm movements; whether these
movements match users’ intentions; the timing of specific wrist and hand
control functions; and users’ opinions regarding overall device
reliability and controller training requirements. In this work, we present
a comprehensive and novel suite of myoelectric prosthesis control
evaluation metrics that better facilitate analysis of device movement
details—spanning measures of task performance, control characteristics,
and user experience. As a case example of their use and research
viability, we applied these metrics in real-time control experimentation.
Here, eight participants without upper limb impairment compared device
control offered by a deep learning-based controller (recurrent
convolutional neural network-based classification with transfer learning,
or RCNN-TL) to that of a commonly used controller (linear discriminant
analysis, or LDA). The participants wore a simulated prosthesis and
performed complex functional tasks across multiple limb positions.
Analysis resulting from our suite of metrics identified 16 instances of a
user-facing problem known as the “limb position effect”. We determined
that RCNN-TL performed the same as or significantly better than LDA in
four such problem instances. We also confirmed that transfer learning can
minimize user training burden. Overall, this study contributes a
multifaceted new suite of control evaluation metrics, along with a guide
to their application, for use in research and testing of myoelectric
controllers today, and potentially for use in broader rehabilitation
technologies of the future.