BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing

TL;DR

BioGAP-Ultra is a versatile, energy-efficient wearable biosensing platform supporting multimodal signal acquisition and embedded AI processing, enabling real-time health monitoring and human-machine interaction in diverse applications.

Contribution

It introduces a significantly enhanced wearable biosensing system with increased storage, more signal modalities, improved connectivity, and integrated real-time analysis, advancing on previous designs.

Findings

Supports five biosignal modalities with synchronized acquisition.

Achieves low power consumption suitable for continuous wearable use.

Demonstrates real-time AI applications with high accuracy.

Abstract

The growing demand for continuous physiological monitoring and human-machine interaction in real-world settings calls for wearable platforms that are flexible, low-power, and capable of on-device intelligence. This work presents BioGAP-Ultra, an advanced multimodal biosensing platform that supports synchronized acquisition of diverse electrophysiological and hemodynamic signals such as EEG, EMG, ECG, and PPG while enabling embedded AI processing at state-of-the-art energy efficiency. BioGAP-Ultra is a major extension of our previous BioGAP design aimed at meeting the rapidly growing requirements of wearable biosensing applications. It features (i) increased on-device storage (x2 SRAM, x4 FLASH), (ii) improved wireless connectivity (supporting up to 1.4 Mbit/s bandwidth, x4 higher than BioGAP), (iii) enhanced number of signal modalities (from 3 to 5) and analog input channels (x2). Further, it is accompanied by a real-time visualization and analysis software suite that supports the hardware design, providing access to raw data and real-time configurability on a mobile phone. Finally, we demonstrate the system's versatility through integration into various wearable form factors: an EEG-PPG headband consuming 32.8 mW, an EMG sleeve at 26.7 mW, and an ECG-PPG chestband requiring only 9.3 mW for continuous acquisition and streaming, tailored for diverse biosignal applications. To showcase its edge-AI capabilities, we further deploy two representative on-device applications: (1) ECG-PPG-based PAT estimation at 8.6 mW, and (2) EMG-ACC-based classification of reach-and-grasp motion phases, achieving 79.9 % $\pm$ 5.7 % accuracy at 23.6 mW. All hardware and software design files are also released open-source with a permissive license.