# Hardware-Efficient EMG Decoding for Next-Generation Hand Prostheses

arXiv: https://arxiv.org/abs/2405.20052 · HTML: https://mlsanity.com/paper/2405.20052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 7 of 7)

- [ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification](https://mlsanity.com/paper/2006.07900/) (2020)
- [TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2203.16336/) (2022)
- [NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC](https://mlsanity.com/paper/2205.06090/) (2022)
- [Closed-Loop Neural Prostheses with On-Chip Intelligence: A Review and A Low-Latency Machine Learning Model for Brain State Detection](https://mlsanity.com/paper/2109.05848/) (2021)
- [Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs](https://mlsanity.com/paper/2204.02362/) (2022)
- [Intelligent and Miniaturized Neural Interfaces: An Emerging Era in Neurotechnology](https://mlsanity.com/paper/2405.10780/) (2024)
- [Real-Time EMG Signal Classification via Recurrent Neural Networks](https://mlsanity.com/paper/2109.05674/) (2021)

## Abstract (arXiv)

Advancements in neural engineering have enabled the development of Robotic Prosthetic Hands (RPHs) aimed at restoring hand functionality. Current commercial RPHs offer limited control through basic on/off commands. Recent progresses in machine learning enable finger movement decoding with higher degrees of freedom, yet the high computational complexity of such models limits their application in portable devices. Future RPH designs must balance portability, low power consumption, and high decoding accuracy to be practical for individuals with disabilities. To this end, we introduce a novel attractor-based neural network to realize on-chip movement decoding for next-generation portable RPHs. The proposed architecture comprises an encoder, an attention layer, an attractor network, and a refinement regressor. We tested our model on four healthy subjects and achieved a decoding accuracy of 80.3%. Our proposed model is over 120 and 50 times more compact compared to state-of-the-art LSTM and CNN models, respectively, with comparable (or superior) decoding accuracy. Therefore, it exhibits minimal hardware complexity and can be effectively integrated as a System-on-Chip.