Magneto-Gyro Wearable Sensor Algorithm for Trunk Sway Estimation During Walking and Running Gait

Trunk sway is a critical gait parameter associated with walking stability and joint loading. This paper introduces a novel algorithm to estimate trunk sway based on magnetometer and gyroscope sensor fusion. An initial experiment was performed to determine the optimal placement of the wearable sensor device on the back and a second experiment was performed to characterize the accuracy of the algorithm. Ten human subjects walked, fast walked, and ran on a treadmill with normal, slightly increased or significantly increased trunk sway. Subjects wore a single magneto-inertial measurement unit (IMU) and a standard set of reflective motion-capture markers on their back. Magneto-gyro algorithm trunk sway estimations were compared with estimations from other common wearable sensor algorithms and errors were determined via comparison with trunk sway measured from motion capture. Overall, the magnetogyro algorithm was the most accurate (RMSE = 1.7 +/- 0.7 degrees) followed by algorithms based on a single three-axis magnetometer (RMSE = 2.5 +/- 1.8 degrees), IMU gradient descent (RMSE = 2.9 +/- 3.4 degrees), a single three-axis gyroscope (RMSE = 3.2 +/- 2.4 degrees), magneto-IMU Kalman filter (RMSE = 8.5 +/- 5.5 degrees), and a single accelerometer (RMSE = 16.7 +/- 11.2 degrees). Optimal placement of a wearable sensor for estimating trunk sway was along the spine between the T7-T12 vertebrae. The presented algorithm based on magnetometer and gyroscope sensor fusion could enable more precise trunk sensing for clinical gait applications outside the laboratory.