BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908710592315392 |
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| author | Frey, Sebastian Spacone, Giusy Cossettini, Andrea Guermandi, Marco Schilk, Philipp Benini, Luca Kartsch, Victor |
| author_facet | Frey, Sebastian Spacone, Giusy Cossettini, Andrea Guermandi, Marco Schilk, Philipp Benini, Luca Kartsch, Victor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13728 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing Frey, Sebastian Spacone, Giusy Cossettini, Andrea Guermandi, Marco Schilk, Philipp Benini, Luca Kartsch, Victor Systems and Control Signal Processing 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. |
| title | BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing |
| topic | Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2508.13728 |