Wrist Angle Estimation Using Two Wearable Inertial Measurement Units

Abstract: Hand-intensive work is closely related to the prevalence of upper body and hand/wrist work-related musculoskeletal disorders (WMSDs) in office work, manufacturing, service industries, as well as the healthcare industry. Some risk factors include vibrations, forceful exertions, heavy manual handling, repetitive motions, and prolonged nonneutral wrist postures. To address the growing WMSD epidemic among various occupational groups, simple-to-use exposure measurements are required. However, common quantitative measurement methods for the hand/wrist, such as electrogoniometry and optical motion capture, are both costly and challenging to use. Small, portable inertial measurement units (IMUs) may therefore be considered as a potentially good, affordable wearable option for measuring hand/wrist posture. However, it is difficult to track the position and orientation of a rigid body due to, among other factors, the IMU sensors' sensitivity to ambient magnetic disturbances. As a result, despite advancements in hardware quality, there is still no widely accepted standard for IMU-based motion capture. This study attempted to address this issue by analysing various orientation algorithms to estimate wrist angle from two IMU sensors and compare them to the electrogoniometer-derived measures, i.e., the gold-standard method in field measurements. Five hand-intensive simulated work tasks, each lasting 4–10 minutes, were completed by six participants. These tasks were chosen to resemble some difficult real-world work conditions closely. The wrist posture of the participants was measured using an electrogoniometer and two IMU sensors that were mounted on top of the electrogoniometer's end blocks. The IMU signal of each sensor was processed using seven different orientation algorithms, and the flexion/extension and radial/ulnar deviation angles between them were extracted and compared to the corresponding electrogoniometer angles. For the best-performing orientation algorithm, which was a first-order complementary filter, the mean cross-correlation coefficient between the two measurements was between 0.41 and 0.90 for the flexion/extension and between 0.19 and 0.53 for the radial/ulnar deviation. The mean absolute error (standard deviation) of the best-performing algorithm for the 10th, 50th, and 90th percentile flexion/extension was 8.38 (8.5), 3.99 (3.4), and 11.93 (10) degrees and for the corresponding percentiles of radial/ulnar deviation it was 9.6 (6.5), 5.5 (4.8), and 10.21 (7.1) degrees.  This result can likely be further improved by applying a better orientation algorithm and reducing measurement artifacts such as sensor vibration. However, this experiment demonstrates the potential of IMU-based wrist angle estimation as a simple measurement tool for occupational risk assessment.