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Kenton R. Kaufman and William J. Shaughnessy

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Wendy J. Hurd, Bernard F. Morrey, and Kenton R. Kaufman


Muscle force must be normalized for between-subjects comparisons of strength to be valid. The most effective method for normalizing muscle strength has not, however, been systematically evaluated.


To evaluate the effects of normalizing muscle strength using a spectrum of anthropometric parameters.






50 uninjured high-school-age baseball pitchers.


Shoulder-rotation strength was tested at 0° and 90° abduction with a handheld dynamometer. Muscle force was normalized to parameters including subject height, weight, height × weight, body-mass index (BMI), forearm length, and forearm length × height.

Outcome Measures:

Statistical analysis included evaluating the coefficient of variation, skewness, and kurtosis of the nonnormalized and normalized muscle force. The most effective normalization method was determined based on the scaling factor that yielded the lowest variability for the data set and promoted the most normal distribution of the data set.


Using body weight to scale muscle force was the most effective anthropometric parameter for normalizing strength values based on the group of statistical measures of variability. BMI, height × weight, and forearm length × weight as scaling factors also yielded less variable values for muscle strength compared with nonnormalized strength, but less consistently than body weight. Height and forearm length were least effective in reducing the variability of the data set relative to nonnormalized muscle force.


This study provides objective support for scaling muscle strength to subject body weight. This approach to normalizing muscle strength uses methods readily accessible to clinicians and researchers and may facilitate the identification of differences in strength between individuals with diverse physical characteristics.

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Emma Fortune, Melissa M.B. Morrow, and Kenton R. Kaufman

Repeated durations of dynamic activity with high ground reaction forces (GRFs) and loading rates (LRs) can be beneficial to bone health. To fully characterize dynamic activity in relation to bone health, field-based measurements of gait kinetics are desirable to assess free-living lower-extremity loading. The study aims were to determine correlations of peak vertical GRF and peak vertical LR with ankle peak vertical accelerations, and of peak resultant GRF and peak resultant LR with ankle peak resultant accelerations, and to compare them to correlations with tibia, thigh, and waist accelerations. GRF data were collected as ten healthy subjects (26 [19–34] years) performed 8–10 walking trials at velocities ranging from 0.19 to 3.05 m/s while wearing ankle, tibia, thigh, and waist accelerometers. While peak vertical accelerations of all locations were positively correlated with peak vertical GRF and LR (r 2 > .53, P < .001), ankle peak vertical accelerations were the most correlated (r 2 > .75, P < .001). All peak resultant accelerations were positively correlated with peak resultant GRF and LR (r 2 > .57, P < .001), with waist peak resultant acceleration being the most correlated (r 2 > .70, P < .001). The results suggest that ankle or waist accelerometers give the most accurate peak GRF and LR estimates and could be useful tools in relating physical activity to bone health.

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Melissa M.B. Morrow, Wendy J. Hurd, Emma Fortune, Vipul Lugade, and Kenton R. Kaufman

This study aimed to define accelerations measured at the waist and lower extremities over a range of gait velocities to provide reference data for choosing the appropriate accelerometer for field-based human activity monitoring studies. Accelerations were measured with a custom activity monitor (± 16g) at the waist, thighs, and ankles in 11 participants over a range of gait velocities from slow walking to running speeds. The cumulative frequencies and peak accelerations were determined. Cumulative acceleration amplitudes for the waist, thighs, and ankles during gait velocities up to 4.8 m/s were within the standard commercial g-range (± 6g) in 99.8%, 99.0%, and 96.5% of the data, respectively. Conversely, peak acceleration amplitudes exceeding the limits of many commercially available activity monitors were observed at the waist, thighs, and ankles, with the highest peaks at the ankles, as expected. At the thighs, and more so at the ankles, nearly 50% of the peak accelerations would not be detected when the gait velocity exceeds a walking velocity. Activity monitor choice is application specific, and investigators should be aware that when measuring high-intensity gait velocity activities with commercial units that impose a ceiling at ± 6g, peak accelerations may not be measured.

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Melissa M.B. Morrow, Bethany Lowndes, Emma Fortune, Kenton R. Kaufman, and M. Susan Hallbeck

The purpose of this study was to validate a commercially available inertial measurement unit (IMU) system against a standard lab-based motion capture system for the measurement of shoulder elevation, elbow flexion, trunk flexion/extension, and neck flexion/extension kinematics. The validation analyses were applied to 6 surgical faculty members performing a standard, simulated surgical training task that mimics minimally invasive surgery. Three-dimensional joint kinematics were simultaneously recorded by an optical motion capture system and an IMU system with 6 sensors placed on the head, chest, and bilateral upper and lower arms. The sensor-to-segment axes alignment was accomplished manually. The IMU neck and trunk IMU flexion/extension angles were accurate to within 2.9 ± 0.9 degrees and 1.6 ± 1.1°, respectively. The IMU shoulder elevation measure was accurate to within 6.8 ± 2.7° and the elbow flexion measure was accurate to within 8.2 ± 2.8°. In the Bland-Altman analyses, there were no significant systematic errors present; however, there was a significant inversely proportional error across all joints. As the gold standard measurement increased, the IMU underestimated the magnitude of the joint angle. This study reports acceptable accuracy of a commercially available IMU system; however, results should be interpreted as protocol specific.