Deployment of Machine Learning Algorithms on Resource-Constrained Hardware Platforms for Prosthetics
Journal article, 2024

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.

prosthetics

embedded systems

machine learning

Motion intent recognition

real-Time

QKeras

classification

TensorFlow

neural networks

quantization

Author

Fabian Just

Chalmers, Electrical Engineering, Systems and control

Center for Bionics and Pain Research

Chiara Ghinami

Center for Bionics and Pain Research

Jan Zbinden

Center for Bionics and Pain Research

Chalmers, Electrical Engineering, Systems and control

Max Jair Ortiz Catalan

Center for Bionics and Pain Research

University of Melbourne

Bionics Institute

Prometei Pain Rehabilitation Center

IEEE Access

2169-3536 (ISSN) 21693536 (eISSN)

Vol. 12 40439-40449 10452358

Subject Categories

Embedded Systems

Robotics

Computer Science

Computer Systems

DOI

10.1109/ACCESS.2024.3371251

More information

Latest update

5/23/2024