Yoon Soocheol, Li-Baboud Ya-Shian, Virts Ann, Bostelman Roger, Shah Mili, Ahmed Nishat
National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
Institute for Soft Matter Synthesis and Metrology, Georgetown University, Washington, DC 20057, USA.
Sensors (Basel). 2025 May 2;25(9):2877. doi: 10.3390/s25092877.
Industrial exoskeletons (a.k.a. wearable robots) have been developed to reduce musculoskeletal fatigue and work injuries. Human joint kinematics and human-robot alignment are important measurements in understanding the effects of industrial exoskeletons. Recently, markerless pose estimation systems based on monocular color (red, green, blue-RGB) and depth cameras are being used to estimate human joint positions. This study analyzes the performance of monocular markerless pose estimation systems on human skeletal joint estimation while wearing exoskeletons. Two pose estimation systems producing RGB and depth images from ten viewpoints are evaluated for one subject in 14 industrial poses. The experiment was repeated for three different types of exoskeletons on the same subject. An optical tracking system (OTS) was used as a reference system. The image acceptance rate was 56% for the RGB, 22% for the depth, and 78% for the OTS pose estimation system. The key sources of pose estimation error were the occlusions from the exoskeletons, industrial poses, and viewpoints. The reference system showed decreased performance when the optical markers were occluded by the exoskeleton or when the markers' position shifted with the exoskeleton. This study performs a systematic comparison of two types of monocular markerless pose estimation systems and an optical tracking system, as well as a proposed metric, based on a tracking quality ratio, to assess whether a skeletal joint estimation would be acceptable for human kinematics analysis in exoskeleton studies.
工业外骨骼（又名可穿戴机器人）已被开发出来以减轻肌肉骨骼疲劳和工伤。人体关节运动学和人机对齐是理解工业外骨骼效果的重要测量指标。最近，基于单目彩色（红、绿、蓝 - RGB）和深度相机的无标记姿态估计系统被用于估计人体关节位置。本研究分析了在穿戴外骨骼时单目无标记姿态估计系统在人体骨骼关节估计方面的性能。针对一名受试者在14种工业姿势下，评估了从十个视角生成RGB和深度图像的两种姿态估计系统。对同一受试者重复该实验，使用三种不同类型的外骨骼。使用光学跟踪系统（OTS）作为参考系统。RGB姿态估计系统的图像接受率为56%，深度姿态估计系统为22%，OTS姿态估计系统为78%。姿态估计误差的主要来源是外骨骼、工业姿势和视角造成的遮挡。当光学标记被外骨骼遮挡或标记位置随外骨骼移动时，参考系统的性能会下降。本研究基于跟踪质量比进行了两种单目无标记姿态估计系统和一种光学跟踪系统的系统比较，以及一种提议的度量标准，以评估骨骼关节估计对于外骨骼研究中的人体运动学分析是否可接受。
