Ryan S. McGinnis

A Highly Miniaturized, Wireless Inertial Measurement Unit for Characterizing the Dynamics of Pitched Baseballs and Softballs

Baseball and softball pitch types are distinguished by the path and speed of the ball which, in turn, are determined by the angular velocity of the ball and the velocity of the ball center at the instant of release from the pitchers hand. While radar guns and video-based motion capture (mocap) resolve ball speed, they provide little information about how the angular velocity of the ball and the velocity of the ball center develop and change during the throwing motion. Moreover, mocap requires measurements in a controlled lab environment and by a skilled technician. This study addresses these shortcomings by introducing a highly miniaturized, wireless inertial measurement unit (IMU) that is embedded in both baseballs and softballs. The resulting “ball-embedded” sensor resolves ball dynamics right on the field of play. Experimental results from ten pitches, five thrown by one softball pitcher and five by one baseball pitcher, demonstrate that this sensor technology can deduce the magnitude and direction of the balls velocity at release to within 4.6% of measurements made using standard mocap. Moreover, the IMU directly measures the angular velocity of the ball, which further enables the analysis of different pitch types.

Monitoring gait in multiple sclerosis with novel wearable motion sensors

Background Mobility impairment is common in people with multiple sclerosis (PwMS) and there is a need to assess mobility in remote settings. Here, we apply a novel wireless, skin-mounted, and conformal inertial sensor (BioStampRC, MC10 Inc.) to examine gait characteristics of PwMS under controlled conditions. We determine the accuracy and precision of BioStampRC in measuring gait kinematics by comparing to contemporary research-grade measurement devices. Methods A total of 45 PwMS, who presented with diverse walking impairment (Mild MS = 15, Moderate MS = 15, Severe MS = 15), and 15 healthy control subjects participated in the study. Participants completed a series of clinical walking tests. During the tests participants were instrumented with BioStampRC and MTx (Xsens, Inc.) sensors on their shanks, as well as an activity monitor GT3X (Actigraph, Inc.) on their non-dominant hip. Shank angular velocity was simultaneously measured with the inertial sensors. Step number and temporal gait parameters were calculated from the data recorded by each sensor. Visual inspection and the MTx served as the reference standards for computing the step number and temporal parameters, respectively. Accuracy (error) and precision (variance of error) was assessed based on absolute and relative metrics. Temporal parameters were compared across groups using ANOVA. Results Mean accuracy±precision for the BioStampRC was 2±2 steps error for step number, 6±9ms error for stride time and 6±7ms error for step time (0.6–2.6% relative error). Swing time had the least accuracy±precision (25±19ms error, 5±4% relative error) among the parameters. GT3X had the least accuracy±precision (8±14% relative error) in step number estimate among the devices. Both MTx and BioStampRC detected significantly distinct gait characteristics between PwMS with different disability levels (p<0.01). Conclusion BioStampRC sensors accurately and precisely measure gait parameters in PwMS across diverse walking impairment levels and detected differences in gait characteristics by disability level in PwMS. This technology has the potential to provide granular monitoring of gait both inside and outside the clinic.

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.

Inertial sensor based method for identifying spherical joint center of rotation

The miniaturized wireless inertial measurement unit (IMU) technology and algorithms presented herein promote rapid and accurate predictions of the center-of-rotation (CoR) for ball/spherical joints. The algorithm improves upon existing IMU-based methods by directly utilizing the measured acceleration and angular velocity provided by the IMU to deduce the CoR in lieu of relying on error-prone velocity and position estimates. Results demonstrate that this new method resolves the position of the CoR to within a 3mm sphere of the true CoR determined by a precision coordinate measuring machine. Such accuracy may render this method attractive for broad use in field, laboratory and clinical settings requiring non-invasive and rapid estimates of joint CoR.

Accuracy of Femur Angles Estimated by IMUs During Clinical Procedures Used to Diagnose Femoroacetabular Impingement

We present a novel method for quantifying femoral orientation angles using a thigh-mounted inertial measurement unit (IMU). The IMU-derived femoral orientation angles reproduce gold-standard motion capture angles to within mean (standard deviation) differences of 0.1 (1.1) degrees on cadaveric specimens during clinical procedures used for the diagnosis of Femoroacetabular Impingement (FAI). The method, which assumes a stationary pelvis, is easy to use, inexpensive, and provides femur motion trajectory data in addition to range of motion measures. These advantages may accelerate the adoption of this technology to inform FAI diagnoses and assess treatment efficacy. To this end, we further investigate the accuracy of hip joint angles calculated using this methodology and assess the sensitivity of our estimates to skin motion artifact during these tasks.

Golf Club Deflection Characteristics as a Function of the Swing Hub Path

This study investigated the relationships between golfer hub path trajectories and interaction kinetics, and club behavior. An equation of motion describing a flexible golf club system was derived and solved to yield time and club position deflection behavior during the downswing. This equation was applied to three diverse subjects whose kinematic and kinetic information was used to drive the simulation. It was determined that there is a relationship between the timing of the maximum interaction torque and the increase in normal force applied to the club and club head deflections. Also, it appears that there is a correlation between degree of radius reduction directly before impact and shaft deflection behavior. The timing of both torque and normal force are directly related to changes in hub path radius thus the effect of hub path geometry on club deflection behavior is secondary. Based upon these findings, a method for fitting shafts to specific swing characteristics was developed that optimized predicted carry distance. These results are based upon limited subjects.

Reconstructing Free-Flight Angular Velocity from a Miniaturized Wireless Accelerometer

The theory governing the torque-free motion of a rigid body is well established, yet direct experimental measurement in the laboratory remains an obvious challenge. This paper addresses this challenge by presenting a novel miniature wireless inertial measurement unit (IMU) that directly measures the motion of a rigid body during free-flight. The IMU incorporates three-axis sensing of acceleration and three-axis sensing of angular velocity with a microcontroller and an RF transceiver for wireless data transmission to a host computer. Experiments consider a rigid body that is spun up by hand and then released into free-flight. The measured rotational dynamics from the IMU are carefully benchmarked against theoretical predictions. This benchmarking reveals that the angular velocity directly measured by the angular rate gyros lies within 6% of that predicted by the (Jacobi elliptic function) solutions to the Euler equations. Moreover, experimentally constructed polhodes elegantly illustrate the expected stable precession for rotations initiated close to the major or minor principal axes and the unstable precession for rotations initiated close to the intermediate axis. We then present a “gyro-free” design that employs a single, triaxial accelerometer to reconstruct the angular velocity during free-flight. A measurement theory is presented and validated experimentally. Results confirm that the angular velocity can be reconstructed with exceedingly small errors (less than 2%) when benchmarked against direct measurements using angular rate gyros. The simpler gyro-free design addresses restrictions imposed by rate gyro cost, size, and measurement range and may enable high-volume commercial applications of this technology in instrumented baseballs, basketballs, golf balls, footballs, soccer balls, softballs, and the like.

A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis

Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment.

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).

The use of a single inertial sensor to estimate 3-dimensional ground reaction force during accelerative running tasks

The purpose of this investigation was to determine the feasibility of using a single inertial measurement unit (IMU) placed on the sacrum to estimate 3-dimensional ground reaction force (F) during linear acceleration and change of direction tasks. Force plate measurements of F and estimates from the proposed IMU method were collected while subjects (n=15) performed a standing sprint start (SS) and a 45° change of direction task (COD). Error in the IMU estimate of step-averaged component and resultant F was quantified by comparison to estimates from the force plate using Bland-Altman 95% limits of agreement (LOA), root mean square error (RMSE), Pearsons product-moment correlation coefficient (r), and the effect size (ES) of the differences between the two systems. RMSE of the IMU estimate of step-average F ranged from 37.70 N to 77.05 N with ES between 0.04 and 0.47 for SS while for COD, RMSE was between 54.19 N to 182.92 N with ES between 0.08 and 1.69. Correlation coefficients between the IMU and force plate measurements were significant (p≤0.05) for all values (r=0.53 to 0.95) except the medio-lateral component of step-average F. The average angular error in the IMU estimate of the orientation of step-average F was ≤10° for all tasks. The results of this study suggest the proposed IMU method may be used to estimate sagittal plane components and magnitude of step-average F during a linear standing sprint start as well as the vertical component and magnitude of step-average F during a 45° change of direction task.