Edge AI for Real-Time Biosignal Processing and Decision-Making

Dear Colleagues,

Wearable and portable sensing systems are increasingly being used to collect physiological and behavioral signals in healthcare, rehabilitation, human–machine interaction, sports monitoring, and assistive robotics. These systems can continuously acquire signals such as electrocardiograms, electromyograms, electroencephalograms, respiration, body temperature, motion, pressure, and tactile information. However, transmitting all raw data to external servers is often limited by communication latency, energy consumption, data privacy, and unreliable network connections.

Edge artificial intelligence provides an effective approach to addressing these limitations by moving signal processing, feature extraction, pattern recognition, and decision-making closer to the sensing device. Recent advances in lightweight neural networks, neuromorphic computing, embedded processors, flexible electronics, and multimodal sensing have enabled wearable systems to perform increasingly complex tasks in real time. Such capabilities are particularly important for applications that require rapid feedback, continuous operation, personalized analysis, and immediate intervention.

This Special Issue focuses on recent developments in edge AI for real-time biosignal processing and intelligent decision-making. It aims to bring together advances in sensing devices, signal processing methods, embedded hardware, machine learning algorithms, and system-level applications. Particular attention will be given to studies that demonstrate practical integration between biosignal acquisition and on-device intelligence, rather than treating sensing hardware and data analysis as separate components.

Original research articles, communications, and review papers are welcome. Topics of interest include, but are not limited to, the following:

• Edge AI for physiological and behavioral signal processing;
• Real-time ECG, EMG, EEG, respiration, motion, pressure, and tactile signal analysis;
• Lightweight machine learning and deep learning models for embedded systems;
• Multimodal biosignal fusion and context-aware decision-making;
• Wearable and flexible sensors integrated with on-device intelligence;
• Neuromorphic and event-driven biosignal processing;
• Low-power hardware and energy-efficient AI architectures;
• Personalized health monitoring and adaptive diagnostic systems;
• Edge intelligence for rehabilitation, prosthetics, and assistive robotics;
• Human–machine interaction based on biosignals, gestures, and tactile information;
• Data privacy, security, reliability, and robustness in edge healthcare systems;
• Closed-loop wearable systems for monitoring, feedback, and intervention;
• Clinical, sports, robotic, and Internet of Medical Things applications.

This Special Issue seeks to provide a focused platform for research connecting biosensing, embedded intelligence, and real-time decision-making. We look forward to receiving contributions that advance the practical development of intelligent, efficient, and reliable edge-enabled biosignal systems.

Dr. Yu Feng
Dr. Ning Li
Dr. Han Huang
Guest Editors

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