Personalization of exercise routines is a crucial factor in helping people achieve their fitness goals. Despite this, many contemporary solutions fail to offer real-time, adaptive feedback tailored to an individual's physiological states. Contemporary fitness solutions often rely only on static plans and do not adjust to factors such as a user's pain thresholds, fatigue levels, or form during a workout routine. This work introduces FlexAI, a multi-modal system that integrates computer vision, physiological sensors (heart rate and voice), and the reasoning capabilities of Large Language Models (LLMs) to deliver real-time, personalized workout guidance. FlexAI continuously monitors a user's physical form and level of exertion, among other parameters, to provide dynamic interventions focused on exercise intensity, rest periods, and motivation. To validate our system, we performed a technical evaluation confirming our models' accuracy and quantifying pipeline latency, alongside an expert review where certified trainers validated the correctness of the LLM's interventions. Furthermore, in a controlled study with 25 participants, FlexAI demonstrated significant improvements over a static, non-adaptive control system. With FlexAI, users reported significantly greater enjoyment, a stronger sense of achievement, and significantly lower levels of boredom and frustration. These results indicate that by integrating multi-modal sensing with LLM-driven reasoning, adaptive systems like FlexAI can create a more engaging and effective workout experience. Our work provides a blueprint for integrating multi-modal sensing with LLM-driven reasoning, demonstrating that it is possible to create adaptive coaching systems that are not only more engaging but also demonstrably reliable.

翻译：运动计划的个性化是帮助人们达成健身目标的关键因素。尽管这一点至关重要，但许多现有解决方案未能提供实时、自适应的反馈，以适配个体的生理状态。当前的健身方案通常依赖静态计划，无法根据用户在锻炼过程中的疼痛阈值、疲劳程度或动作姿势等因素进行动态调整。本研究提出FlexAI——一种多模态系统，它融合了计算机视觉、生理传感器（心率与语音）以及大型语言模型（LLMs）的推理能力，以提供实时的个性化运动指导。FlexAI持续监测用户的身体姿势、发力程度等参数，针对运动强度、休息间隔及激励措施进行动态干预。为验证该系统，我们进行了技术评估（确认模型准确性与流水线延迟），并邀请认证教练开展专家评审，以验证LLM干预措施的正确性。此外，在包含25名参与者的对照实验中，FlexAI相较于静态非自适应对照组展现出显著优势。使用FlexAI的用户报告了更高的愉悦感、更强的成就感，以及更低的无聊与挫败感。这些结果表明，通过将多模态感知与LLM驱动的推理相结合，FlexAI等自适应系统能够打造更吸引人、更有效的健身体验。本研究为融合多模态感知与LLM推理提供了蓝图，证明构建兼具高参与度与可靠性的自适应指导系统是切实可行的。