构建实时姿势评估系统，解决居家健身无监督难题。
Real-Time Feedback and Benchmark Dataset for Isometric Pose Evaluation
- 基于图神经网络实现动作姿态实时分析
- 发布超3600段视频数据集，覆盖6类动作
- 新评测指标兼顾准确率、定位与置信度
等长运动因其便捷、私密和低设备依赖性受到欢迎，但常因依赖不可靠的数字内容而缺乏专家指导，导致姿势错误、受伤及训练中断。为此，我们提出一种实时等长姿势评估反馈系统。贡献包括发布迄今最大的多类别等长运动视频数据集，包含超过3,600个片段，涵盖六种动作的正确与错误变体。为支持稳健评估，我们在该数据集上基准测试了多种前沿模型（包括基于图的网络），并引入一种新型三部分评价指标，综合衡量分类准确率、错误定位能力与模型置信度。结果表明，该系统显著提升了家庭健身中智能个性化训练的可行性。专家级诊断可直接反馈给用户，拓展了系统在康复、理疗及其他涉及身体运动领域的应用潜力。
原文摘要 · Abstract (English)
Isometric exercises appeal to individuals seeking convenience, privacy, and minimal dependence on equipments. However, such fitness training is often overdependent on unreliable digital media content instead of expert supervision, introducing serious risks, including incorrect posture, injury, and disengagement due to lack of corrective feedback. To address these challenges, we present a real-time feedback system for assessing isometric poses. Our contributions include the release of the largest multiclass isometric exercise video dataset to date, comprising over 3,600 clips across six poses with correct and incorrect variations. To support robust evaluation, we benchmark state-of-the-art models-including graph-based networks-on this dataset and introduce a novel three-part metric that captures classification accuracy, mistake localization, and model confidence. Our results enhance the feasibility of intelligent and personalized exercise training systems for home workouts. This expert-level diagnosis, delivered directly to the users, also expands the potential applications of these systems to rehabilitation, physiotherapy, and various other fitness disciplines that involve physical motion.
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