Exploring Human-AI Collaboration in E-Textile Design: A Case Study on Flex Sensor Placement for Shoulder Motion Detection

2026-03-13

AI Summary

This study addresses the lack of a unified design methodology for flexible sensor placement in e-textiles by conducting a case study on shoulder motion detection, systematically comparing three design paradigms: human-only, AI-only, and human-AI collaboration. Integrating large language models (LLMs), flexible sensing technologies, and domain knowledge from human anatomy and biomechanics, the research employs controlled experiments and mixed-methods analysis to uncover how designers’ expertise levels, feedback granularity, and abstraction depth influence collaborative outcomes. Results demonstrate that novice designers, when assisted by LLMs, significantly improve their performance to match that of experts, whereas expert designers experience a decline in effectiveness. Furthermore, incremental, observation-driven feedback proves superior to holistic redesign or anatomy-based instructions. This work presents the first systematic evaluation and validation of LLMs’ potential as design assistants in e-textile development.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Flex sensors are widely used in e-textiles for detecting joint motions and, subsequently, full-body movements. A critical initial step in utilizing these sensors is determining the optimal placement on the body to accurately capture human motions. This task requires a combination of expertise in fields such as anatomy, biomechanics, and textile design, which is seldom found in a single practitioner. Generative AI, such as Large Language Models (LLMs), has recently shown promise in facilitating design. However, to our knowledge, the extent to which LLMs can aid in the e-textile design process remains largely unexplored in the literature. To address this open question, we conducted a case study focusing on shoulder motion detection using flex sensors. We enlisted three human designers to participate in an experiment involving human-AI collaborative design. We examined design efficiency across three scenarios: designs produced by LLMs alone, by humans alone, and through collaboration between LLMs and human designers. Our quantitative and qualitative analyses revealed an intriguing relationship between expertise and outcomes: the least experienced human designer achieved continuous improvement through collaboration, ultimately matching the best performance achieved by humans alone, whereas the most experienced human designer experienced a decline in performance. Additionally, the effectiveness of human-AI collaboration is affected by the granularity of feedback - incremental adjustments outperformed sweeping redesigns - and the level of abstraction, with observation-oriented feedback producing better outcomes than prescriptive anatomical directives. These findings offer valuable insights into the opportunities and challenges associated with human-AI collaborative e-textile design.

Problem

• e-textile design
• flex sensor placement
• shoulder motion detection
• human-AI collaboration
• motion sensing

Innovation

• Human-AI collaboration
• e-textile design
• flex sensor placement
• Large Language Models (LLMs)
• design feedback granularity