Call for Papers
2026 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2026)
IEEE SMC Workshop:
NeuroWearX for Empowered Co-Intelligence:
Advancing Human–Machine Interfaces by Integrating Biological, Physical, and Spatial Intelligence with Generative AI
Overview
Wearables, assistive robots, and smart IoT systems are rapidly becoming part of everyday life. However, many of today’s wearable computing and human–machine interfaces still feel “smart, yet not quite helpful.” They are often fragmented, difficult to personalize, and frequently struggle to transform rich but noisy multimodal signals, such as physiology (e.g., heart rate, neural/muscle activity), body movement, and environmental context, into reliable, meaningful real-world support. At the same time, generative AI and foundation/world models are reshaping the landscape, shifting the paradigm beyond isolated sensors and single-purpose algorithms toward integrated, adaptive, context-aware Human–AI–Machine systems.
To make the use of these technologies feel like a natural extension of ourselves, this workshop brings together researchers and practitioners across biosensing and measurement, neuroscience, AI, robotics, ubiquitous computing, and human-centered design to define the next frontier of Empowered Co-Intelligence: wearable interfaces that fuse three complementary “intelligences”—(1) biological intelligence, to infer human state and intent by modeling how people naturally think and behave; (2) physical/embodied intelligence, which understands body dynamics and real-world physics so interaction and assistance remain safe and effective; and (3) spatial intelligence, to leverage nearby sensors, IoT devices and smart environments for continuous situational awareness. These capabilities are further amplified by generative AI—models trained on large-scale data that can integrate heterogeneous signals to predict, reason, and adapt—enabling systems to become more personalized and context-aware. Together, these capabilities can overcome the limits of noisy on-body sensing and constrained wearable computing by turning fragmented measurements into coherent, actionable assistance.
Our vision is an AI that operates quietly in the background, like an invisible layer of artificial cortex, running in parallel with our own cerebral cortex and coordinating across different lobes: not simply following rules, but learning your patterns, anticipating your needs, and adjusting in real time. The result is 24/7 support for thinking, decision-making, and physical action that feels intuitive, seamless, and requires minimal cognitive effort.
Beyond new algorithms, we also emphasize human-centered evaluation, trust, accessibility, and inclusive augmentation, with the goal of accelerating research that advances wearable computing into reliable, scalable, and equitable systems—technologies that truly co-evolve with users over time.
Topics of Interest
We welcome research papers, short/WiP papers, and position/vision papers on (but not limited to) the following areas aligned with multimodal perception–decision–action cycles, shared autonomy, personalization, safety, and real-world robustness on wearable technologies.
(A) Biological intelligence: sensing human state & intent
● Multimodal biosensing: EEG/EMG/ECG/EDA/PPG/respiration, inertial + physiological fusion
● Robust biosignal decoding in-the-wild: drift handling, motion artifacts, missing data, calibration-free methods
● Intent recognition and user-state estimation (fatigue, stress, attention, readiness, motor intent)
● Personalized adaptation across users: domain adaptation, continual learning, few-shot personalization
● Privacy-preserving on-body learning and secure biosignal pipelines
(B) Physical intelligence: embodied assistance & safe action
● Embodied AI for wearable augmentation: biomechanics, dynamics, control, and safe shared autonomy
● Wearable robotics and human–robot physical interaction (exoskeletons, prostheses, assistive devices)
● Safety, stability, and fail-safe design in closed-loop wearable control
● Human factors and ergonomics for physical assistance; workload-aware support
● Verification/validation of embodied policies for assistive wearable systems
(C) Spatial intelligence: context from environments, robots & smart IoT
● Context-aware wearable computing using smart environments, robots, and IoT integration
● Scene understanding for assistance: activity context, objects, layout, hazards, and social context
● Multi-device sensing orchestration (wearable + phone + AR + ambient sensors)
● Real-world robustness and interoperability across heterogeneous devices
(D) Generative AI & foundation/world models for wearables
● Generative AI / foundation models for wearable time-series, biosignal representation learning, multimodal fusion
● World models for prediction, planning, and co-adaptation in human-centered wearable interaction
● Edge/on-body inference: efficiency, compression, distillation, and low-power deployment
● Uncertainty-aware assistance, reliable decision-making, and “AI-in-the-background” support
(E) Human-centered NeuroDesign, evaluation & impact
● Human-centered interface design: intuitive/subconscious interaction, trust, transparency, explainability
● UX evaluation in real-world settings: accessibility, equity, inclusive augmentation
● Ethical, privacy, and governance considerations for always-on wearable intelligence
● Application domains: assistive augmentation, rehabilitation, health monitoring, everyday support
Submission Instructions
● Submission deadline: March 22, 2026
● Submission site at 2026 IEEE International Conference on Systems, Man, and Cybernetics (Papercept):
https://conf.papercept.net/conferences/scripts/start.pl
● Submission code: dt3i1
(Use the submission code "dt3i1" during the Papercept submission process to route your paper to this workshop.)
● Please follow the IEEE SMC 2026 submission guidelines and formatting requirements.
IEEE SMC 2026 submission guidelines:
https://www.ieeesmc2026.org/call-for-papers
Best regards,
Organizing Committee
🧠 NeuroWearX Workshop @ IEEE SMC 2026