Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics.

Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics.

Finite element models predict in vitro vertebral body compressive strength better than quantitative computed tomography.

The correlation between bone mineral density and vertebral strength is not based on mechanical principles and thus the method cannot reflect the effects of subtle geometric features and densitometric inhomogeneities that may substantially affect vertebral strength. Finite element models derived from quantitative computed tomography (QCT) scans overcome such limitations. The overall goal of this study was to establish that QCT-based "voxel" finite element models are better predictors of vertebral compressive strength than QCT measures of bone mineral density with or without measures of cross-sectional area. QCT scans were taken of 13 vertebral bodies excised from 13 cadavers (L1-L4; age: 37-87 years; M = 6, F = 7) and used to calculate bone mineral density (BMD(QCT)). The QCT voxel data were converted into linearly elastic finite element models of each vertebra, from which measures of vertebral stiffness and strength were computed. The vertebrae were biomechanically tested in compression to measure strength. Vertebral strength was positively correlated with the finite element measures of strength (r(2) = 0.86, P < 0.0001) and stiffness (r(2) = 0.82, P < 0.0001), the product of BMD(QCT) and vertebral minimum cross-sectional area (r(2) = 0.65, P = 0.0008), and BMD(QCT) alone (r(2) = 0.53, P = 0.005). These results demonstrate that highly automated "voxel" finite element models are superior to correlation-based QCT methods in predicting vertebral compressive strength and therefore offer great promise for improvement of clinical fracture risk assessment.

Relationship between axial and bending behaviors of the human thoracolumbar vertebra.

STUDY DESIGN: The authors studied the mechanical behavior of vertebrae through the use of finite element analyses. OBJECTIVES: To determine the relation between axial and bending rigidity, and to determine the geometric and densitometric factors that affect this relation. SUMMARY OF BACKGROUND DATA: Metrics of vertebral body mechanical properties in bending have not been established despite evidence that anterior bending loads play a significant role in osteoporotic vertebral fracture. METHODS: Voxel-based finite element models were generated using quantitative computed tomography (QCT) scans of 18 human cadaveric vertebral bodies, and both axial and bending rigidities of the vertebra were computed. Both rigidity measures and their ratio were correlated with vertebral geometric and densitometric factors obtained from the QCT scans. RESULTS: Bending rigidity was moderately correlated with axial rigidity (r2 = 0.69) and strongly correlated with the product of axial rigidity and vertebral anteroposterior depth squared (r2 = 0.88). The ratio of bending to axial rigidity was independent of bone mineral density (P = 0.20) but was moderately correlated with the square of vertebral depth (r2 = 0.69). CONCLUSIONS: Vertebral anteroposterior depth plays an important role in bending rigidity. The scatter in the correlation between bending and axial rigidity suggests that some individuals can have vertebrae with a normal axial stiffness but an abnormally low bending stiffness. Because whole-bone stiffness is indicative of bone strength, these results support the concept that use of more than one metric of vertebral strength, for example, compression and bending strengths, may improve osteoporotic fracture risk prediction.

Quantitative computed tomography-based finite element models of the human lumbar vertebral body: effect of element size on stiffness, damage, and fracture strength predictions.

This study investigated the numerical convergence characteristics of specimen-specific "voxel-based" finite element models of 14 excised human cadaveric lumbar vertebral bodies (age: 37-87; M = 6, F = 8) that were generated automatically from clinical-type CT scans. With eventual clinical applications in mind, the ability of the model stiffness to predict the experimentally measured compressive fracture strength of the vertebral bodies was also assessed. The stiffness of "low"-resolution models (3 x 3 x 3 mm element size) was on average only 4% greater (p = 0.03) than for "high"-resolution models (1 x 1 x 1.5 mm) despite interspecimen variations that varied over four-fold. Damage predictions using low- vs high-resolution models were significantly different (p = 0.01) at loads corresponding to an overall strain of 0.5%. Both the high (r2 = 0.94) and low (r2 = 0.92) resolution model stiffness values were highly correlated with the experimentally measured ultimate strength values. Because vertebral stiffness variations in the population are much greater than those that arise from differences in voxel size, these results indicate that imaging resolution is not critical in cross-sectional studies of this parameter. However, longitudinal studies that seek to track more subtle changes in stiffness over time should account for the small but highly significant effects of voxel size. These results also demonstrate that an automated voxel-based finite element modeling technique may provide an excellent noninvasive assessment of vertebral strength.