# Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition

arXiv: https://arxiv.org/abs/2503.13492 · HTML: https://mlsanity.com/paper/2503.13492/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems](https://mlsanity.com/paper/2104.13386/) (2021)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020)
- [Reservoir computing approaches for representation and classification of multivariate time series](https://mlsanity.com/paper/1803.07870/) (2018)
- [Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications](https://mlsanity.com/paper/2007.05657/) (2020)
- [Neuromorphic electronic circuits for building autonomous cognitive systems](https://mlsanity.com/paper/1403.6428/) (2014)
- [Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor](https://mlsanity.com/paper/1911.05521/) (2019)
- [Kernel methods on spike train space for neuroscience: a tutorial](https://mlsanity.com/paper/1302.5964/) (2013)
- [Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition](https://mlsanity.com/paper/1811.07516/) (2018)
- [Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks](https://mlsanity.com/paper/1611.01557/) (2016)
- [Evaluation of Encoding Schemes on Ubiquitous Sensor Signal for Spiking Neural Network](https://mlsanity.com/paper/2407.09260/) (2024)
- [Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures](https://mlsanity.com/paper/2309.16425/) (2023)

## Abstract (arXiv)

Wearable health devices have a strong demand in real-time biomedical signal processing. However traditional methods often require data transmission to centralized processing unit with substantial computational resources after collecting it from edge devices. Neuromorphic computing is an emerging field that seeks to design specialized hardware for computing systems inspired by the structure, function, and dynamics of the human brain, offering significant advantages in latency and power consumption. This paper explores a novel neuromorphic implementation approach for gesture recognition by extracting spatiotemporal spiking information from surface electromyography (sEMG) data in an event-driven manner. At the same time, the network was designed by implementing a simple-structured and hardware-friendly Physical Reservoir Computing (PRC) framework called Rotating Neuron Reservoir (RNR) within the domain of Spiking neural network (SNN). The spiking RNR (sRNR) is promising to pipeline an innovative solution to compact embedded wearable systems, enabling low-latency, real-time processing directly at the sensor level. The proposed system was validated by an open-access large-scale sEMG database and achieved an average classification accuracy of 74.6% and 80.3% using a classical machine learning classifier and a delta learning rule algorithm respectively. While the delta learning rule could be fully spiking and implementable on neuromorphic chips, the proposed gesture recognition system demonstrates the potential for near-sensor low-latency processing.