  Human-machine interaction is gaining traction in rehabilitation tasks, such
as controlling prosthetic hands or robotic arms. Gesture recognition exploiting
surface electromyographic (sEMG) signals is one of the most promising
approaches, given that sEMG signal acquisition is non-invasive and is directly
related to muscle contraction. However, the analysis of these signals still
presents many challenges since similar gestures result in similar muscle
contractions. Thus the resulting signal shapes are almost identical, leading to
low classification accuracy. To tackle this challenge, complex neural networks
are employed, which require large memory footprints, consume relatively high
energy and limit the maximum battery life of devices used for classification.
This work addresses this problem with the introduction of the Bioformers. This
new family of ultra-small attention-based architectures approaches
state-of-the-art performance while reducing the number of parameters and
operations of 4.9X. Additionally, by introducing a new inter-subjects
pre-training, we improve the accuracy of our best Bioformer by 3.39%, matching
state-of-the-art accuracy without any additional inference cost. Deploying our
best performing Bioformer on a Parallel, Ultra-Low Power (PULP) microcontroller
unit (MCU), the GreenWaves GAP8, we achieve an inference latency and energy of
2.72 ms and 0.14 mJ, respectively, 8.0X lower than the previous
state-of-the-art neural network, while occupying just 94.2 kB of memory.
