Pūioio：基于智能手机的设备端实时自动运动次数计数系统
Pūioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting System
AI总结：
本文提出并实现了名为 Pūioio 的智能手机端侧实时运动次数计数系统，融合姿态估计、光流和状态机，仅凭摄像头即可高精度计数深蹲、俯卧撑和引体向上。
摘要：
自动运动次数计数在从个人健康到康复的整个体能领域都有应用。受移动电话的普及和追踪身体活动益处的推动，本研究探索了在智能手机上仅使用设备端推理进行实时运动次数计数的可行性。在本文中，在广泛概述了现有自动运动次数计数方法之后，我们介绍了一个基于深度学习的智能手机运动次数计数系统，该系统由五个组件组成：（1）姿态估计，（2）阈值化，（3）光流，（4）状态机，以及（5）计数器。该系统随后通过一个名为 Pūioio 的跨平台移动应用程序实现，该应用程序仅使用智能手机摄像头实时追踪三种标准运动的次数：深蹲、俯卧撑和引体向上。所提出的系统通过预先录制的个人运动视频数据集以及受试者实时运动测试进行了评估。评估结果表明，该系统在真实世界测试中准确率为 98.89%，在预先录制的数据集上评估时准确率最高达 98.85%。这使得它成为现有解决方案的一种有效、低成本且方便的替代方案，因为所提出的系统具有最低的硬件要求，不需要任何可穿戴设备、特定传感器或网络连接。
英文摘要：
Automated exercise repetition counting has applications across the physical fitness realm, from personal health to rehabilitation. Motivated by the ubiquity of mobile phones and the benefits of tracking physical activity, this study explored the feasibility of counting exercise repetitions in real-time, using only on-device inference, on smartphones. In this work, after providing an extensive overview of the state-of-the-art automatic exercise repetition counting methods, we introduce a deep learning based exercise repetition counting system for smartphones consisting of five components: (1) Pose estimation, (2) Thresholding, (3) Optical flow, (4) State machine, and (5) Counter. The system is then implemented via a cross-platform mobile application named Pūioio that uses only the smartphone camera to track repetitions in real time for three standard exercises: Squats, Push-ups, and Pull-ups. The proposed system was evaluated via a dataset of pre-recorded videos of individuals exercising as well as testing by subjects exercising in real time. Evaluation results indicated the system was 98.89% accurate in real-world tests and up to 98.85% when evaluated via the pre-recorded dataset. This makes it an effective, low-cost, and convenient alternative to existing solutions since the proposed system has minimal hardware requirements without requiring any wearable or specific sensors or network connectivity.
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。
