AidWear
AI-enabled active trans-femoral prosthesis
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projects
Revalexo
AI-enabled active lower-body rehabilitation exoskeleton
publications
RT-ST-GCN: Enabling Realtime Continual Inference at the Edge
Published in 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), 2024
We propose a hardware-driven optimization, termed RT-ST-GCN, which scales down the computational bottleneck of ST-GCN to achieve realtime predictions up to 50 fps.
Recommended citation: Yudayev, M., Filtjens, B., & Balasch, J. (2024, September). "RT-ST-GCN: Enabling Realtime Continual Inference at the Edge." In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE. doi: 10.1109/MLSP58920.2024.10734754.
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talks
Closing the Loop in Multimodal Edge AI for Healthcare: Accelerating the development of realtime closed-loop systems
Published:
A half-day interactive tutorial session to expose the wider audience of the CPS-IoT community to my recently released HERMES framework for:
- Realtime intelligent closed-loop healthtech devices and applications
- Reliable distributed multimodal physiological sensor data collection for AI workflows and longitudinal studies
- Continuous sensing and monitoring systems in the free-living out-of-the-lab environments