标题： 使用智能设备的独木舟划桨质量评估：初步机器学习研究 显示英文标题

标题： Canoe Paddling Quality Assessment Using Smart Devices: Preliminary Machine Learning Study

摘要： 每年有超过2200万美国人参与划船相关活动，为2020年价值24亿美元的全球划船运动市场做出贡献。 尽管这项运动很受欢迎，但机器学习（ML）的整合仍然有限，并且受到教练费用和专业设备成本的阻碍。 本研究提出了一种基于人工智能的教练系统，该系统使用基于运动数据训练的机器学习模型，并通过大型语言模型（LLM）提供划水反馈。 参与者通过与纽约大学混凝土船队的合作招募。 运动数据在两个会话中收集，一个会话是不理想的姿势，另一个会 session 是纠正的技术，使用固定在运动带上的苹果手表和智能手机。 数据经过划水分割和特征提取。 机器学习模型，包括支持向量分类器、随机森林、梯度提升和极端随机树，在原始和工程特征上进行了训练。 开发了一个基于网络的界面来可视化划水质量和提供LLM反馈。 四位参与者进行了八次试验，获得了66个划水样本。 极端随机树模型在五折交叉验证下取得了最高的性能，F分数为0.9496。 网络界面成功提供了定量指标和定性反馈。 靠近手腕的传感器放置提高了数据质量。 初步结果表明，智能手表和智能手机可以实现低成本、易于获取的传统划船教学替代方案。 虽然受样本量限制，但该研究证明了使用消费设备和机器学习来支持划水改进和技术提高的可行性。 显示英文摘要

摘要： Over 22 million Americans participate in paddling-related activities annually, contributing to a global paddlesports market valued at 2.4 billion US dollars in 2020. Despite its popularity, the sport has seen limited integration of machine learning (ML) and remains hindered by the cost of coaching and specialized equipment. This study presents a novel AI-based coaching system that uses ML models trained on motion data and delivers stroke feedback via a large language model (LLM). Participants were recruited through a collaboration with the NYU Concrete Canoe Team. Motion data were collected across two sessions, one with suboptimal form and one with corrected technique, using Apple Watches and smartphones secured in sport straps. The data underwent stroke segmentation and feature extraction. ML models, including Support Vector Classifier, Random Forest, Gradient Boosting, and Extremely Randomized Trees, were trained on both raw and engineered features. A web based interface was developed to visualize stroke quality and deliver LLM-based feedback. Across four participants, eight trials yielded 66 stroke samples. The Extremely Randomized Tree model achieved the highest performance with an F score of 0.9496 under five fold cross validation. The web interface successfully provided both quantitative metrics and qualitative feedback. Sensor placement near the wrists improved data quality. Preliminary results indicate that smartwatches and smartphones can enable low cost, accessible alternatives to traditional paddling instruction. While limited by sample size, the study demonstrates the feasibility of using consumer devices and ML to support stroke refinement and technique improvement.