Rachel V. Vitali

Quantifying performance and effects of load carriage during a challenging balancing task using an array of wireless inertial sensors

We utilize an array of wireless inertial measurement units (IMUs) to measure the movements of subjects (n=30) traversing an outdoor balance beam (zigzag and sloping) as quickly as possible both with and without load (20.5kg). Our objectives are: (1) to use IMU array data to calculate metrics that quantify performance (speed and stability) and (2) to investigate the effects of load on performance. We hypothesize that added load significantly decreases subject speed yet results in increased stability of subject movements. We propose and evaluate five performance metrics: (1) time to cross beam (less time=more speed), (2) percentage of total time spent in double support (more double support time=more stable), (3) stride duration (longer stride duration=more stable), (4) ratio of sacrum M-L to A-P acceleration (lower ratio=less lateral balance corrections=more stable), and (5) M-L torso range of motion (smaller range of motion=less balance corrections=more stable). We find that the total time to cross the beam increases with load (t=4.85, p<0.001). Stability metrics also change significantly with load, all indicating increased stability. In particular, double support time increases (t=6.04, p<0.001), stride duration increases (t=3.436, p=0.002), the ratio of sacrum acceleration RMS decreases (t=-5.56, p<0.001), and the M-L torso lean range of motion decreases (t=-2.82, p=0.009). Overall, the IMU array successfully measures subject movement and gait parameters that reveal the trade-off between speed and stability in this highly dynamic balance task.

Method for Estimating Three-Dimensional Knee Rotations Using Two Inertial Measurement Units: Validation with a Coordinate Measurement Machine

Three-dimensional rotations across the human knee serve as important markers of knee health and performance in multiple contexts including human mobility, worker safety and health, athletic performance, and warfighter performance. While knee rotations can be estimated using optical motion capture, that method is largely limited to the laboratory and small capture volumes. These limitations may be overcome by deploying wearable inertial measurement units (IMUs). The objective of this study is to present a new IMU-based method for estimating 3D knee rotations and to benchmark the accuracy of the results using an instrumented mechanical linkage. The method employs data from shank- and thigh-mounted IMUs and a vector constraint for the medial-lateral axis of the knee during periods when the knee joint functions predominantly as a hinge. The method is carefully validated using data from high precision optical encoders in a mechanism that replicates 3D knee rotations spanning (1) pure flexion/extension, (2) pure internal/external rotation, (3) pure abduction/adduction, and (4) combinations of all three rotations. Regardless of the movement type, the IMU-derived estimates of 3D knee rotations replicate the truth data with high confidence (RMS error < 4° and correlation coefficient r≥0.94).

Validation of Complementary Filter Based IMU Data Fusion for Tracking Torso Angle and Rifle Orientation

Up-down and rifle aiming maneuvers are common tasks employed by soldiers and athletes. The movements underlying these tasks largely determine performance success, which motivates the need for a noninvasive and portable means for movement quantification. We answer this need by exploiting body-worn and rifle-mounted miniature inertial measurement units (IMUs) for measuring torso and rifle motions during up-down and aiming tasks. The IMUs incorporate MEMS accelerometers and angular rate gyros that measure translational acceleration and angular velocity, respectively. Both sensors enable independent estimates of the orientation of the IMU and thus, the orientation of a subject’s torso and rifle. Herein, we establish the accuracy of a complementary filter which fuses these estimates for tracking torso and rifle orientation by comparing IMU-derived and motion capture-derived (MOCAP) torso pitch angles and rifle elevation and azimuthal angles during four up-down and rifle aiming trials for each of 16 subjects (64 trials total). The up-down trials consist of five maximal effort get-down-get-up cycles (from standing to lying prone back to standing), while the rifle aiming trials consist of rapidly aiming five times at two targets 15 feet from the subject and 180 degrees apart. Results reveal that this filtering technique yields warfighter torso pitch angles that remain within 0.55 degrees of MOCAP estimates and rifle elevation and azimuthal angles that remain within 0.44 and 1.26 degrees on average, respectively, for the 64 trials analyzed. We further examine potential remaining error sources and limitations of this filtering approach. These promising results point to the future use of this technology for quantifying motion in naturalistic environments. Their use may be extended to other applications (e.g., sports training and remote health monitoring) where noninvasive, inexpensive, and accurate methods for reliable orientation estimation are similarly desired.Copyright

Load-embedded inertial measurement unit reveals lifting performance

Manual lifting of loads arises in many occupations as well as in activities of daily living. Prior studies explore lifting biomechanics and conditions implicated in lifting-induced injuries through laboratory-based experimental methods. This study introduces a new measurement method using load-embedded inertial measurement units (IMUs) to evaluate lifting tasks in varied environments outside of the laboratory. An example vertical load lifting task is considered that is included in an outdoor obstacle course. The IMU data, in the form of the load acceleration and angular velocity, is used to estimate load vertical velocity and three lifting performance metrics: the lifting time (speed), power, and motion smoothness. Large qualitative differences in these parameters distinguish exemplar high and low performance trials. These differences are further supported by subsequent statistical analyses of twenty three trials (including a total of 115 total lift/lower cycles) from fourteen healthy participants. Results reveal that lifting time is strongly correlated with lifting power (as expected) but also correlated with motion smoothness. Thus, participants who lift rapidly do so with significantly greater power using motions that minimize motion jerk.

Estimating Stair Running Performance Using Inertial Sensors

Stair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time.

Quantifying performance on an outdoor agility drill using foot-mounted inertial measurement units

Running agility is required for many sports and other physical tasks that demand rapid changes in body direction. Quantifying agility skill remains a challenge because measuring rapid changes of direction and quantifying agility skill from those measurements are difficult to do in ways that replicate real task/game play situations. The objectives of this study were to define and to measure agility performance for a (five-cone) agility drill used within a military obstacle course using data harvested from two foot-mounted inertial measurement units (IMUs). Thirty-two recreational athletes ran an agility drill while wearing two IMUs secured to the tops of their athletic shoes. The recorded acceleration and angular rates yield estimates of the trajectories, velocities and accelerations of both feet as well as an estimate of the horizontal velocity of the body mass center. Four agility performance metrics were proposed and studied including: 1) agility drill time, 2) horizontal body speed, 3) foot trajectory turning radius, and 4) tangential body acceleration. Additionally, the average horizontal ground reaction during each footfall was estimated. We hypothesized that shorter agility drill performance time would be observed with small turning radii and large tangential acceleration ranges and body speeds. Kruskal-Wallis and mean rank post-hoc statistical analyses revealed that shorter agility drill performance times were observed with smaller turning radii and larger tangential acceleration ranges and body speeds, as hypothesized. Moreover, measurements revealed the strategies that distinguish high versus low performers. Relative to low performers, high performers used sharper turns, larger changes in body speed (larger tangential acceleration ranges), and shorter duration footfalls that generated larger horizontal ground reactions during the turn phases. Overall, this study advances the use of foot-mounted IMUs to quantify agility performance in contextually-relevant settings (e.g., field of play, training facilities, obstacle courses, etc.).