Live Demonstration: Neuromorphic Radar for Gesture Recognition

2025-08-05

AI Summary

Traditional radar-based gesture recognition (HGR) suffers from high power consumption, large memory footprint, and excessive computational overhead due to continuous signal sampling. To address this, we propose an event-driven neuromorphic radar system. Our key innovation is the first application of biologically inspired asynchronous sigma-delta encoding to radar intermediate-frequency (IF) signal processing, enabling motion-triggered, sparse pulse-event generation without spectrogram reconstruction. A lightweight neural network executes real-time inference directly on a Cortex-M0 microcontroller. The system integrates a 24-GHz Doppler radar frontend with a custom neuromorphic sampler that activates only upon detection of valid motion. Evaluated on a dataset comprising seven subjects performing five gesture classes, the system achieves >85% real-time recognition accuracy. It significantly reduces power consumption, memory usage, and computational load compared to conventional frame-based approaches—demonstrating the feasibility of ultra-low-power, edge-deployable neuromorphic radar sensing.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We present a neuromorphic radar framework for real-time, low-power hand gesture recognition (HGR) using an event-driven architecture inspired by biological sensing. Our system comprises a 24 GHz Doppler radar front-end and a custom neuromorphic sampler that converts intermediate-frequency (IF) signals into sparse spike-based representations via asynchronous sigma-delta encoding. These events are directly processed by a lightweight neural network deployed on a Cortex-M0 microcontroller, enabling low-latency inference without requiring spectrogram reconstruction. Unlike conventional radar HGR pipelines that continuously sample and process data, our architecture activates only when meaningful motion is detected, significantly reducing memory, power, and computation overhead. Evaluated on a dataset of five gestures collected from seven users, our system achieves > 85% real-time accuracy. To the best of our knowledge, this is the first work that employs bio-inspired asynchronous sigma-delta encoding and an event-driven processing framework for radar-based HGR.

Problem

• Real-time low-power hand gesture recognition
• Event-driven neuromorphic radar processing
• Reducing memory and computation overhead

Innovation

• Event-driven neuromorphic radar for gesture recognition
• Asynchronous sigma-delta encoding for sparse spikes
• Lightweight neural network on Cortex-M0 microcontroller