Variations in Concurrent Validity of Two Independent Inertial Measurement Units Compared to Gold Standard for Upper Body Posture during Computerised Device Use

Abstract

Inertial measurement units (IMUs) may provide an objective method for measuring posture during computer use, but research is needed to validate IMUs' accuracy. We examine the concurrent validity of two different IMU systems in measuring three-dimensional (3D) upper body posture relative to a motion capture system (Mocap) as a potential device to assess postures outside a laboratory environment. We used 3D Mocap and two IMU systems (Wi-Fi and Bluetooth) to capture the upper body posture of twenty-six individuals during three physical computer working conditions (monitor correct, monitor raised, and laptop). Coefficient of determination (R²) and root-mean-square error (RMSE) compared IMUs to Mocap. Head/neck segment [HN], upper trunk segment [UTS], and joint angle [HN-UTS] were the primary variables. Wi-Fi IMUs demonstrated high validity for HN and UTS (sagittal plane) and HN-UTS (frontal plane) for all conditions, and for HN rotation movements (both for the monitor correct and monitor raised conditions), others moderate to poor. Bluetooth IMUs for HN, and UTS (sagittal plane) for the monitor correct, laptop, and monitor raised conditions were moderate. Frontal plane movements except UTS (monitor correct and laptop) and all rotation had poor validity. Both IMU systems were affected by gyroscopic drift with sporadic data loss in Bluetooth IMUs. Wi-Fi IMUs had more acceptable accuracy when measuring upper body posture during computer use compared to Mocap, except for trunk rotations. Variation in IMU systems' performance suggests validation in the task-specific movement(s) is essential.

Keywords: computer use; inertial measurement unit; posture; upper body posture; validation; wearables.

Figure 1

IMU rigid plates and retro-reflective marker placement for head and upper trunk segments.

Figure 2

Upper trunk movement (averages) for twenty-six participants showing the relationship between the Wi-Fi IMU, Bluetooth IMU, and motion capture (Mocap) data across 14 min of typing during the first condition (included monitor correct, monitor raised, or laptop) across three axes with standard deviation denoted by shaded areas. Note that for flexion/extension and lateral flexion, the Wifi IMUs throughout the time-series provide data that is consistent with motion capture, with drift for the Bluetooth IMU after approximately the 8th minute for lateral flexion and approximately the 4th minute for flexion/extension. For rotation, the Wifi IMU provides similar data to motion capture, but the Bluetooth IMU displays significant offset at time zero and drift.

Figure 3

Rotation movement for HN segment from one participant during typing for 14 min demonstrating the magnitude of gyroscopic drift for the Bluetooth IMU compared to the Wi-Fi IMU and motion capture.