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The zero-velocity update (ZUPT) method has become a popular approach to estimate foot kinematics from foot worn inertial measurement units (IMUs) during walking and running. However, the accuracy of the ZUPT method for stride parameters at sprinting speeds remains unknown, specifically when using sensors with characteristics well suited for sprinting (i.e., high accelerometer and gyroscope ranges and sampling rates). Seventeen participants performed 80-meter track sprints while wearing a Blue Trident IMeasureU IMU. Two cameras, at 20 and 70 meters from the start, were used to validate the ZUPT method on a stride-by-stride and on a cumulative distance basis. In particular, the validity of the ZUPT method was assessed for: (1) estimating a single stride length attained near the end of an 80m sprint (i.e., stride at 70m); (2) estimating cumulative distance from ∼20 to ∼70 m; and (3) estimating total distance traveled for an 80-meter track sprint. Individual stride length errors at the 70-meter mark were within -6% to 3%, with a bias of -0.27%. Cumulative distance errors were within -4 to 2%, with biases ranging from -0.85 to -1.22%. The results of this study demonstrate the ZUPT method provides accurate estimates of stride length and cumulative distance traveled for sprinting speeds. [ABSTRACT FROM AUTHOR]

Human lower-limb kinematic measurements are critical for many applications including gait analysis, enhancing athletic performance, reducing or monitoring injury risk, augmenting warfighter performance, and monitoring elderly fall risk, among others. We present a new method to estimate lower-limb kinematics using an error-state Kalman filter that utilizes an array of body-worn inertial measurement units (IMUs) and four kinematic constraints. We evaluate the method on a simplified 3-body model of the lower limbs (pelvis and two legs) during walking using data from simulation and experiment. Evaluation on this 3-body model permits direct evaluation of the ErKF method without several confounding error sources from human subjects (e.g., soft tissue artefacts and determination of anatomical frames). RMS differences for the three estimated hip joint angles all remain below 0.2 degrees compared to simulation and 1.4 degrees compared to experimental optical motion capture (MOCAP). RMS differences for stride length and step width remain within 1% and 4%, respectively compared to simulation and 7% and 5%, respectively compared to experiment (MOCAP). The results are particularly important because they foretell future success in advancing this approach to more complex models for human movement. In particular, our future work aims to extend this approach to a 7-body model of the human lower limbs composed of the pelvis, thighs, shanks, and feet. [ABSTRACT FROM AUTHOR]

This study introduces a new method to understand how added load affects human performance across a broad range of athletic tasks (ten obstacles) embedded in an outdoor obstacle course. The method employs an array of wearable inertial measurement units (IMUs) to wirelessly record the movements of major body segments to derive obstacle-specific metrics of performance. The effects of load are demonstrated on (N = 22) participants who each complete the obstacle course under four conditions including unloaded (twice) and with loads of 15% and 30% of their body weight (a total of 88 trials across the group of participants). The IMU-derived performance metrics reveal marked degradations in performance with increasing load across eight of the ten obstacles. Overall, this study demonstrates the significant potential in using this wearable technology to evaluate human performance across multiple tasks and, simultaneously, the adverse effects of body-borne loads on performance. The study addresses a major need of military organizations worldwide that frequently employ standardized obstacle courses to understand how added loads influence warfighter performance. Importantly, the findings and conclusions drawn from IMU data would not be possible using traditional timing metrics used to evaluate task performance. [ABSTRACT FROM AUTHOR]