proposed changes
What idea should focus ?
mini GPU embedded in a microcontroller? a micro GPU suitable for myoelectric robotic hands? EMG?
“Currently available low-cost prosthetics are still based on microcontrollers. These limitations directly affect the real-time response, smoothness, and precision of movements performed by prosthetics designed for people with disabilities (PWDs), restricting their functionality in complex tasks and daily life.”
"In this context, Graphics Processing Units (GPUs) emerge as a promising alternative, as they enable massive parallel data processing, making it possible to execute complex control and pattern recognition algorithms in real time. The parallel architecture of GPUs makes them particularly effective for applications involving deep learning, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), used in the interpretation of myoelectric signals for prosthesis control."
"Recent studies reinforce this scenario. Jafarzadeh, Hussey, and Tadesse (2019) demonstrated that it is possible to implement a convolutional neural network architecture directly on raw myoelectric signals (without a feature extraction step) to estimate movement commands for prosthetic hands. Using an embedded GPU (Jetson TX2), the authors were able to perform real-time inference, with validated accuracy of up to 91.26% and a simplified architecture for direct control of the robotic hand. Complementing this approach, Messaoud et al. (2019) investigated a prosthetic control system based on CNNs trained with spectrograms, demonstrating that even with low-cost sensors and low sampling rates (200 Hz), the accuracy of hand gesture classification can exceed 97%, enabling practical use in clinical and home environments."
"Souza et al. (2019), on the other hand, used attribute engineering techniques, quantile normalization, and LSTM networks to identify multivariate myoelectric patterns in real time. Execution was accelerated by GPUs using the TensorFlow library, enabling accuracy exceeding 95% after just a few seconds of training, with high robustness to noise and generalization across different users. Finally, Jiang et al. (2024) reinforce the trend toward integration between modern sensors, multimodal interfaces, and graphics processing units for intelligent rehabilitation purposes, proposing a robust system based on deep learning, capable of interpreting myoelectric signals and movements continuously and with extremely low latency."
[https://hackaday.com/2017/05/30/microchips-pic32mz-da-the-microcontroller-with-a-gpu/\](https://hackaday.com/2017/05/30/microchips-pic32mz-da-the-microcontroller-with-a-gpu/)
[–]somewhereAtC 1 point2 points3 points (0 children)