# Static Hand Gesture Recognition for American Sign Language using Neuromorphic Hardware

arXiv: https://arxiv.org/abs/2207.12559 · HTML: https://mlsanity.com/paper/2207.12559/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 21 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Built on by (most cited first, 7 of 7)

- [Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge AI Accelerators](https://mlsanity.com/paper/2403.08792/) (2024)
- [Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware](https://mlsanity.com/paper/2210.05006/) (2022)
- [Towards Efficient Deployment of Hybrid SNNs on Neuromorphic and Edge AI Hardware](https://mlsanity.com/paper/2407.08704/) (2024)
- [Sign Language Recognition using Parallel Bidirectional Reservoir Computing](https://mlsanity.com/paper/2512.19451/) (2025)
- [TransONet: Automatic Segmentation of Vasculature in Computed Tomographic Angiograms Using Deep Learning](https://mlsanity.com/paper/2311.10328/) (2023)
- [Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning](https://mlsanity.com/paper/2311.16001/) (2023)
- [Neuromorphic visual attention for Sign-language recognition on SpiNNaker](https://mlsanity.com/paper/2605.06005/) (2026)

## Abstract (arXiv)

In this paper, we develop four spiking neural network (SNN) models for two static American Sign Language (ASL) hand gesture classification tasks, i.e., the ASL Alphabet and ASL Digits. The SNN models are deployed on Intel's neuromorphic platform, Loihi, and then compared against equivalent deep neural network (DNN) models deployed on an edge computing device, the Intel Neural Compute Stick 2 (NCS2). We perform a comprehensive comparison between the two systems in terms of accuracy, latency, power consumption, and energy. The best DNN model achieves an accuracy of 99.93% on the ASL Alphabet dataset, whereas the best performing SNN model has an accuracy of 99.30%. For the ASL-Digits dataset, the best DNN model achieves an accuracy of 99.76% accuracy while the SNN achieves 99.03%. Moreover, our obtained experimental results show that the Loihi neuromorphic hardware implementations achieve up to 20.64x and 4.10x reduction in power consumption and energy, respectively, when compared to NCS2.