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Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry and optical motion capture, while reliable, are expensive and impractical for field use. In contrast, small inertial measurement units (IMUs) may provide a cost-effective, time-efficient, and user-friendly alternative for measuring hand/wrist posture during real work. This study compared six orientation algorithms for estimating wrist angles with an electrogoniometer, the current gold standard in field settings. Six participants performed five simulated hand-intensive work tasks (involving considerable wrist velocity and/or hand force) and one standardised hand movement. Three multiplicative Kalman filter algorithms with different smoothers and constraints showed the highest agreement with the goniometer. These algorithms exhibited median correlation coefficients of 0.75–0.78 for flexion/extension and 0.64 for radial/ulnar deviation across the six subjects and five tasks. They also ranked in the top three for the lowest mean absolute differences from the goniometer at the 10th, 50th, and 90th percentiles of wrist flexion/extension (9.3°, 2.9°, and 7.4°, respectively). Although the results of this study are not fully acceptable for practical field use, especially for some work tasks, they indicate that IMU-based wrist angle estimation may be useful in occupational risk assessments after further improvements.

As an alternative to traditional remote controller, research on vision-based hand gesture recognition is being actively conducted in the field of interaction between human and unmanned aerial vehicle (UAV). However, vision-based gesture system has a challenging problem in recognizing the motion of dynamic gesture because it is difficult to estimate the pose of multi-dimensional hand gestures in 2D images. This leads to complex algorithms, including tracking in addition to detection, to recognize dynamic gestures, but they are not suitable for human–UAV interaction (HUI) systems that require safe design with high real-time performance. Therefore, in this paper, we propose a hybrid hand gesture system that combines an inertial measurement unit (IMU)-based motion capture system and a vision-based gesture system to increase real-time performance. First, IMU-based commands and vision-based commands are divided according to whether drone operation commands are continuously input. Second, IMU-based control commands are intuitively mapped to allow the UAV to move in the same direction by utilizing estimated orientation sensed by a thumb-mounted micro-IMU, and vision-based control commands are mapped with hand’s appearance through real-time object detection. The proposed system is verified in a simulation environment through efficiency evaluation with dynamic gestures of the existing vision-based system in addition to usability comparison with traditional joystick controller conducted for applicants with no experience in manipulation. As a result, it proves that it is a safer and more intuitive HUI design with a 0.089 ms processing speed and average lap time that takes about 19 s less than the joystick controller. In other words, it shows that it is viable as an alternative to existing HUI.