Authors: Oleh Mykhailovych Hrytsenko, Iryna Volodymyrivna Lysenko, Denys Ivanovych Sydorenko, Viktoriia Andriivna Kravets
Abstract: The integration of Edge-AI with wearable biomedical devices like Myobioscan is reshaping the landscape of real-time clinical diagnostics and patient monitoring. This paper explores how embedding artificial intelligence at the device edge enables low-latency, high-frequency processing of biosignals such as electromyography (EMG), electrocardiography (ECG), and motion patterns. The combination of Myobioscan’s compact sensor technology with on-device AI accelerators facilitates proactive health assessments, early anomaly detection, and decentralized clinical interventions. By reviewing recent deployments and experimental models, this study identifies key performance metrics, data handling architectures, and regulatory considerations in deploying Edge-AI for mobile health. The findings point toward a scalable and responsive healthcare ecosystem driven by distributed intelligence at the physiological interface.
