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Most balance assessment studies using inertial measurement units (IMUs) in smartphones use a body strap and assume the alignment of the smartphone with the anatomical axes. To replace the need for a body strap, we have used an anatomical alignment method that employs a calibration maneuver and Principal Component Analysis (PCA) so that the smartphone can be held by the user in a comfortable position. The objectives of this study were to determine if correlations existed between angular velocity scores derived from a handheld smartphone with PCA functional alignment vs. a smartphone placed in a strap with assumed alignment, and to analyze acceleration score differences across balance poses of increasing difficulty. The handheld and body strap smartphones exhibited moderately to strongly correlated angular velocity scores in the calibration maneuver (r = 0.487-0.983, p < 0.001). Additionally, the handheld smartphone with PCA functional calibration successfully detected significant variance between pose type scores for anteroposterior, mediolateral, and superoinferior acceleration data ( p < 0.001).

Balance assessment, or posturography, tracks and prevents health complications for a variety of groups with balance impairment, including the elderly population and patients with traumatic brain injury. Wearables can revolutionize state-of-the-art posturography methods, which have recently shifted focus to clinical validation of strictly positioned inertial measurement units (IMUs) as replacements for force-plate systems. Yet, modern anatomical calibration (i.e., sensor-to-segment alignment) methods have not been utilized in inertial-based posturography studies. Functional calibration methods can replace the need for strict placement of inertial measurement units, which may be tedious or confusing for certain users. In this study, balance-related metrics from a smartwatch IMU were tested against a strictly placed IMU after using a functional calibration method. The smartwatch and strictly placed IMUs were strongly correlated in clinically relevant posturography scores (r = 0.861-0.970, p < 0.001). Additionally, the smartwatch was able to detect significant variance ( p < 0.001) between pose-type scores from the mediolateral (ML) acceleration data and anterior-posterior (AP) rotation data. With this calibration method, a large problem with inertial-based posturography has been addressed, and wearable, "at-home" balance-assessment technology is within possibility.

This study's objectives were to: (1) assess whether dual energy X-ray absorptiometry (DXA)-mass inverse dynamics (ID) alters predictions of youth pitching arm kinetics and (2) investigate correlations between kinetics and body composition. Eighteen 10- to 11-year-olds pitched 10 fastballs. DXA scans were conducted to obtain participant-specific upper arm, forearm, and hand masses. Pitching arm segment masses and kinetics calculated with scaled and DXA masses were compared with paired t-tests and correlations were investigated with linear regression. Hand (p < 0.001) and upper arm (p < 0.001) DXA masses were greater, while forearm (p < 0.001) DXA masses were lesser, than their scaled masses. Shoulder compressive force (p < 0.001), internal rotation torque (p < 0.001), and horizontal adduction torque (p = 0.002) increased when using DXA masses. Shoulder compressive force correlated with body mass (p < 0.001) and body mass index (BMI; p = 0.002) and elbow varus torque correlated with body mass (p < 0.05). The main conclusions were that (1) using participant-specific mass ratios leads to different predictions of injury-related pitching arm kinetics and, thus, may improve our understanding of injury risk factors; and (2) pitching arm kinetics were correlated with body composition measures and a relatively high total body mass and/or BMI may increase shoulder and/or elbow injury risk.

Many baseball pitching studies have used inverse dynamics to assess throwing arm kinetics as high and repetitive kinetics are thought to be linked to pitching injuries. However, prior studies have not used participant-specific body segment inertial parameters (BSIPs), which are thought to improve analysis of high-acceleration motions and overweight participants. This study's objectives were to (1) calculate participant-specific BSIPs using dual energy X-ray absorptiometry (DXA) measures, (2) compare inverse dynamic calculations of kinetics determined by DXA-calculated BSIPs (full DXA-driven inverse dynamics) against kinetics using the standard inverse dynamics approach with scaled BSIPs (scaled inverse dynamics), and (3) examine associations between full DXA-driven kinetics and overweight indices: body mass index (BMI) and segment mass index (SMI). Eighteen participants (10-11 years old) threw 10 fastballs that were recorded for motion analysis. DXA scans were used to calculate participant-specific BSIPs (mass, center of mass, radii of gyration) for each pitching arm segment (upper arm, forearm, hand), BMI, and SMI. The hypotheses were addressed with t-tests and linear regression analyses. The major results were that (1) DXA-calculated BSIPs differed from scaled BSIPs for each pitching arm segment; (2) calculations for shoulder, but not elbow, kinetics differed between the full DXA-driven and scaled inverse dynamics analyses; and (3) full DXA-driven inverse dynamics calculations for shoulder kinetics were more often associated with SMI than BMI. Results suggest that using participant-specific BSIPs and pitching arm, SMIs may improve evidence-based injury prevention guidelines for youth pitchers., (Copyright © 2022 by ASME.)