PlanFitting：基于大语言模型驱动的对话式智能体的个性化运动规划
PlanFitting: Personalized Exercise Planning with Large Language Model-driven Conversational Agent
AI总结：
本研究针对个性化运动计划制定成本高、难获取的问题，提出由大语言模型驱动的对话智能体PlanFitting，通过自由对话挖掘用户需求以生成符合运动指南的个性化周计划，经多维度评估验证其有效性，并探讨了未来设计方向。
摘要：
制定个性化且可执行的运动计划通常需要与专家反复沟通，这一过程成本高昂，且很多人难以获得相关服务。本研究探讨了大语言模型（LLM）在应对这些挑战方面的能力。我们提出了PlanFitting，这是一款由LLM驱动的对话式智能体，可协助用户制定并完善个性化的每周运动计划。通过与用户进行自由形式对话，PlanFitting有助于挖掘用户的目标、可用时间及潜在障碍，使人们能够生成符合既定运动指南的个性化运动计划。我们的研究包含用户研究、内在评估和专家评估，结果证明PlanFitting能够引导用户制定量身定制、可执行且循证的计划。我们还讨论了LLM驱动的对话式智能体未来的设计方向，以打造更符合运动原则、适配个人约束条件的计划。
英文摘要：
Creating personalized and actionable exercise plans often requires iteration with experts, which can be costly and inaccessible to many individuals. This work explores the capabilities of Large Language Models (LLMs) in addressing these challenges. We present PlanFitting, an LLM-driven conversational agent that assists users in creating and refining personalized weekly exercise plans. By engaging users in free-form conversations, PlanFitting helps elicit users' goals, availabilities, and potential obstacles, and enables individuals to generate personalized exercise plans aligned with established exercise guidelines. Our study -- involving a user study, intrinsic evaluation, and expert evaluation -- demonstrated PlanFitting's ability to guide users to create tailored, actionable, and evidence-based plans. We discuss future design opportunities for LLM-driven conversational agents to create plans that better comply with exercise principles and accommodate personal constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
