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Within the field of electromyography-based (EMG) gesture recognition,disparities exist between the offline accuracy reported in the literature andthe real-time usability of a classifier. This gap mainly stems from twofactors: 1) The absence of a controller, making the data collected dissimilarto actual control. 2) The difficulty of including the four main dynamic factors(gesture intensity, limb position, electrode shift, and transient changes inthe signal), as including their permutations drastically increases the amountof data to be recorded. Contrarily, online datasets are limited to the exactEMG-based controller used to record them, necessitating the recording of a newdataset for each control method or variant to be tested. Consequently, thispaper proposes a new type of dataset to serve as an intermediate betweenoffline and online datasets, by recording the data using a real-timeexperimental protocol. The protocol, performed in virtual reality, includes thefour main dynamic factors and uses an EMG-independent controller to guidemovements. This EMG-independent feedback ensures that the user is in-the-loopduring recording, while enabling the resulting dynamic dataset to be used as anEMG-based benchmark. The dataset is comprised of 20 able-bodied participantscompleting three to four sessions over a period of 14 to 21 days. The abilityof the dynamic dataset to serve as a benchmark is leveraged to evaluate theimpact of different recalibration techniques for long-term (across-day) gesturerecognition, including a novel algorithm, named TADANN. TADANN consistently andsignificantly (p<0.05) outperforms using fine-tuning as the recalibrationtechnique.

A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition