Can design help robots to improve human health and safety?


History:


The seafood processing industry has long been a vital component of the global food supply chain, transforming raw fish and seafood into consumable products through filleting, deboning, shucking, freezing, and packaging. Historically, this work was done manually, requiring intense physical labor in cold, wet, and fast-paced environments.

As demand for processed seafood grew, the industry adopted mechanized systems to improve efficiency, yet human labor remained essential for tasks requiring dexterity, decision-making, and adaptability—such as identifying bone fragments, sorting fish by quality, or trimming fillets to precise specifications. However, repetitive tasks, extended exposure to cold temperatures, and awkward, strain-inducing movements have led to high injury rates, worker fatigue, and increased turnover.

In recent years, the industry has faced labor shortages, rising costs, and sustainability challenges, prompting an urgent need for intelligent automation that enhances human productivity rather than replacing workers outright. The Gymnast_CoBot project builds on this need by reimagining industrial robotics to support, rather than strain, the workforce.


Info:


The Gymnast_CoBot project rethinks human-machine interaction by applying human-centered design principles to industrial robotics. Instead of merely automating tasks, the system enhances workplace ergonomics, allowing robots to dynamically adapt to worker movements, fatigue levels, and task complexity.

We explored how motion tracking, sensor integration, and real-time feedback could create a more intuitive and health-conscious work environment. Inspired by fitness equipment, where machines assist rather than replace human effort, we designed a collaborative system where robots support physical labor without increasing long-term strain.

  • Shared Workspace User Interface: A projected UI system displays robot actions, human-robot interaction insights, and upcoming movements directly onto relevant surfaces.
  • Gesture Interaction: Workers communicate hands-free using wristband-tracked arm gestures instead of touchscreens, improving usability in glove-wearing and high-movement environments.
  • Sensor-Based Human Monitoring: Height, weight, heart rate, movement, and fatigue estimation inform real-time robotic adjustments.
  • User-Centered Research: Studied workplace ergonomics, movement patterns, and common sources of injury to inform the product roadmap.


By focusing on human needs and behavior, we created a data-driven framework for developing robotics that enhance both worker safety and productivity.


Key Contributions:



  • Project Documentation – Developed technical reports, UX research, and product vision materials for further development.

  • Industrial Research – Studied archival materials, industrial workflows, movement patterns, and workplace health risks to develop the robotic system.

  • Prototyping Robotic Attachments – Designed and printed projector-based UI systems and data-collecting attachments for real-time human-robot interaction.

  • Interaction Design – Developed gesture-based control systems and intuitive interfaces to improve engagement.

  • Reverse Engineering VR Hardware - Analyzed and modified VR positioning systems for model simulation and precise human-motion tracking. 


In collaboration with Experience Design Lab with the support of National Science Foundation under the award number 1928654.


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