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.

通过计算机视觉和惯性测量单元 (IMU) 等广泛应用的技术以及最近在临床和研究环境中日益流行，来评估人体的身体活动。然而，为了增加患者在实验室外的跟踪可行性，有必要减少运动采集设备。在这方面，IMU 设备和单一摄像头系统是有希望的解决方案。此外，还需要开发能够在野外环境中识别和处理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。