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This study performed a concurrent comparison of two walking speed estimation methods using shank- and foot-mounted inertial measurement units (IMUs). Based on the cyclic gait pattern of the stance leg during walking, data was segmented into a series of individual stride cycles. The angular velocity and linear accelerations of the shank and foot over each of these cycles were then integrated to determine the walking speed. The evaluation was performed on 10 healthy subjects during treadmill walking where known treadmill speeds were compared with the estimated walking speeds under normal and toe-out walking conditions. Results from the shank-mounted IMU sensor yielded more accurate walking speed estimates, with a maximum root mean square estimation error (RMSE) of 0.09 m/s in normal walking and 0.10 m/s in toe-out conditions; while the foot-mounted IMU sensors yielded a maximum RMSE of 0.14 m/s in normal walking and 0.26 m/s in toe-out conditions. Shank-mounted IMU sensors may prove to be of great benefit in accurately estimating walking speeds in patients whose gait is characterized by abnormal foot motions.

This study validated the feasibility of inertial sensors in estimating lower limb joint kinematics during stair ambulation in healthy older adults and stroke survivors. Three dimensional motion data were collected using an inertial sensor-based system from 9 persons with stroke and 9 healthy older adults as they ascended and descended a staircase at a self-selected pace. The measured joint angles were compared with a laboratory-based motion capture system by computing differences in range of motion (RoM), grand mean error, standard deviation, and coefficients of multiple correlations. For stroke survivors, differences in RoM measurements between these two systems were determined to be 3.3 ± 8.1°, while the highest correlations were found in the estimation of sagittal plane joint angles after offset correction. Results suggest that the inertial sensor system is suitable for estimating major joint angles in healthy older adults as well as the RoM for stroke survivors. New calibration procedures are necessary for applying the technology to a stroke population.

With the increasing interest of using inertial measurement units (IMU) in human biomechanics studies, methods dealing with inertial sensor measurement errors become more and more important. Pre-test calibration and in-test error compensation are commonly used to minimize the sensor errors and improve the accuracy of the walking speed estimation results. However, the performance of a given sensor error compensation method does not only depend on the accuracy of the calibration or the sensor error evaluation, but also strongly relies on the selected sensor error model. The best performance could be achieved only when the essential components of sensor errors are included and compensated. Two new sensor error models, with the concerns about sensor acceleration measurement biases and sensor attachment misalignment, have been developed. The performance of these two error models were evaluated in the shank-mounted IMU-based walking speed/inclination estimation algorithm with a comparison of an existing error model. The treadmill walking experiment, conducted at both level and incline conditions, demonstrated the importance of sensor error model selection on the spatio-temporal gait parameter estimation performance. Accurate walking inclination estimation was made possible with a newly developed sensor error model.

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