Deployment of Machine Learning Algorithms on Resource-Constrained
Hardware Platforms for Prosthetics
- Fabian Just ,
- Chiara ghinami ,
- Jan Zbinden ,
- Max Ortiz-Catalan
Abstract
Motion intent recognition for controlling prosthetic systems has long
relied on machine learning algorithms. Artificial neural networks have
shown great promise for solving such nonlinear classification tasks,
making them a viable method for this purpose. To bring these advanced
methods and algorithms beyond the confines of the laboratory and into
the daily lives of prosthetic users, self-contained embedded systems are
essential. However, embedded systems face constraints in size,
computational power, memory footprint, and power consumption, as they
must be non-intrusive and discreetly integrated into commercial
prosthetic components. One promising approach to tackle these challenges
is to use network quantization, which allows complying with limitations
without significant loss in accuracy. Here, we compare network
quantization performance for self-contained systems using TensorFlow
Lite and the recently developed QKeras platform. Due to internal
libraries, the use of TensorFlow Lite led to a 8 times higher flash
memory usage than that of the unquantized reference network,
disadvantageous for self-contained prosthetic systems. In response, we
offer open-source code solutions that leverage the QKeras platform,
effectively reducing flash memory requirements by 24 times compared to
Tensorflow Lite. Additionally, we conducted a comprehensive comparison
of state-of-the-art microcontrollers. Our results reveal that the
adoption of new architectures offers substantial reductions in inference
time and power consumption. These improvements pave the way for
real-time decoding of motor intent using more advanced machine learning
algorithms for daily life usage, possibly enabling more reliable and
precise control for prosthetic users.