用视觉姿态关键点模拟惯性传感器，实时识别潜水员异常状态。
Robotic Classification of Divers' Swimming States using Visual Pose Keypoints as IMUs
- 通过3D姿态关键点生成伪惯性数据，替代水下失效的穿戴式传感器。
- 在模拟紧急场景中实现高精度异常行为识别，保障潜水安全。
- 适合水下机器人监测、潜水救援系统研发人员参考。
传统人体活动识别依赖图像分析或可穿戴惯性测量单元（IMUs），但在水下环境中效果不佳。本文提出一种新型混合方法，利用计算机视觉生成高保真运动数据，从3D人体关节关键点流中构建‘伪IMU’，克服了水下无线信号衰减问题，该问题严重影响可穿戴传感器与自主水下航行器（AUV）的通信。本方法应用于识别潜水员异常行为，预警心搏骤停等医疗紧急情况——这是导致潜水死亡的主要原因。将分类器集成至AUV onboard，并在模拟应急场景中测试，验证了该方法在提升机器人监控能力与潜水员安全保障方面的有效性。
原文摘要 · Abstract (English)
Traditional human activity recognition uses either direct image analysis or data from wearable inertial measurement units (IMUs), but can be ineffective in challenging underwater environments. We introduce a novel hybrid approach that bridges this gap to monitor scuba diver safety. Our method leverages computer vision to generate high-fidelity motion data, effectively creating a ``pseudo-IMU'' from a stream of 3D human joint keypoints. This technique circumvents the critical problem of wireless signal attenuation in water, which plagues conventional diver-worn sensors communicating with an Autonomous Underwater Vehicle (AUV). We apply this system to the vital task of identifying anomalous scuba diver behavior that signals the onset of a medical emergency such as cardiac arrest -- a leading cause of scuba diving fatalities. By integrating our classifier onboard an AUV and conducting experiments with simulated distress scenarios, we demonstrate the utility and effectiveness of our method for advancing robotic monitoring and diver safety.
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