Robotic Classification of Divers' Swimming States using Visual Pose Keypoints as IMUs
This addresses diver safety by enabling robotic monitoring in challenging underwater conditions where traditional sensors fail, though it appears incremental as it combines existing vision and IMU concepts for a specific domain.
The paper tackled the problem of monitoring scuba diver safety in underwater environments by developing a hybrid approach that uses visual pose keypoints to create pseudo-IMU data, enabling classification of anomalous swimming states like those signaling cardiac arrest, with experiments conducted on simulated distress scenarios.
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.