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.

翻译：柔性传感器广泛应用于电子织物中，用于检测关节运动及全身动作。使用这些传感器的关键初始步骤是确定其在人体上的最佳布局，以准确捕捉人体运动。该任务需要结合解剖学、生物力学和纺织品设计等领域的专业知识，而单一从业者很少具备所有这些知识。生成式人工智能，如大语言模型（LLMs），近年来在辅助设计方面展现出潜力。然而，据我们所知，LLMs能在多大程度上辅助电子织物设计过程，在现有文献中仍属未充分探索的领域。针对这一开放性问题，我们开展了一项聚焦于使用柔性传感器进行肩部运动检测的案例研究。我们邀请三位人类设计师参与一项涉及人机协作设计的实验。我们考察了三种场景下的设计效率：仅由LLMs生成的设计、仅由人类完成的设计，以及LLMs与人类设计师协作完成的设计。我们的定量与定性分析揭示了一个关于专业水平与结果之间耐人寻味的关系：经验最少的人类设计师通过协作实现了持续改进，最终达到了与仅由人类完成的最佳设计相当的水平；而经验最丰富的人类设计师在协作中性能反而有所下降。此外，人机协作的效果受到反馈粒度（渐进式调整优于全面重新设计）和抽象层次（基于观察的反馈比规定性的解剖学指令产生更好的结果）的影响。这些发现为理解人机协作电子织物设计相关的机遇与挑战提供了有价值的见解。