不同深度学习架构下可穿戴设备与单摄像头视频用于实验室外上肢活动识别的比较研究
A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures
AI总结：
本研究比较了可穿戴IMU与单摄像头视频在不同深度学习架构下用于实验室外上肢活动识别的效果，旨在确定机器学习系统的理想输入，以提高患者追踪的可行性。
摘要：
在临床和研究环境中，广泛使用各种计算机视觉解决方案以及近期的高端惯性测量单元（IMU）来评估人体身体活动已变得越来越普遍。然而，为了提高在实验室外环境中患者追踪的可行性，有必要使用数量较少的设备进行运动采集。在此背景下，基于IMU的可穿戴设备和单摄像头系统是有前景的解决方案。此外，需要开发能够识别并消化野外临床相关数据的机器学习系统，因此确定这些系统的理想输入至关重要。
英文摘要：
The use of a wide range of computer vision solutions, and more recently high-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical activity in clinical and research settings. Nevertheless, to increase the feasibility of patient tracking in out-of-the-lab settings, it is necessary to use a reduced number of devices for movement acquisition. Promising solutions in this context are IMU-based wearables and single camera systems. Additionally, the development of machine learning systems able to recognize and digest clinically relevant data in-the-wild is needed, and therefore determining the ideal input to those is crucial.
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。
