Biology subjects

Grabowski, A. M.

Publications and source records attributed to Grabowski, A. M..

4 recordsLinked to original sources

bioRxiv2025

Modeling the Biomechanical Features Affecting the Metabolic Rate of Walking with a Powered Ankle-Foot Prosthesis

For individuals with unilateral transtibial amputation, powered ankle-foot prostheses have the potential to reduce the metabolic rate of walking, which could contribute to improvements in mobility and quality of life; however, physiological improvements have not been consistently demonstrated in experimental studies. To improve our understanding of the biomechanical mechanisms that drive metabolic rate outcomes, we used a machine learning approach to model the relationship between multimodal biomechanical factors and the metabolic rate of walking with a powered ankle-foot prosthesis. Our model included 50 features describing spatiotemporal parameters, step-to-step transition work, joint kinematics, muscle activity, ground reaction forces, prosthesis settings, and subject characteristics, and resulted in a pseudo-R2 of 0.986. The features with the largest effect on metabolic rate were peak unaffected side ankle inversion angle, leading affected leg positive work during the step-to-step transition, and peak affected knee extension angle. Accumulated local effects plots were used to visualize the direction and magnitude of the relationship between each feature and the metabolic rate of walking. This work furthers our knowledge about the biomechanical and physiological response to powered ankle-foot prosthesis use and could assist in developing new strategies to drive reductions in metabolic rate.

bioengineering↗

bioRxiv2023

Characterizing the mechanical properties of low-profile prosthetic feet

Passive-elastic prosthetic feet are manufactured with different numerical stiffness categories that are prescribed based on the body mass and activity level of the user, but the mechanical properties, such as the stiffness values and hysteresis are not typically reported by the manufacturer. Since the mechanical properties of passive-elastic prosthetic feet can affect the walking biomechanics of people with transtibial or transfemoral amputation, characterizing these properties would provide objective values for comparison and aid the prescription of prosthetic feet. Therefore, we characterized the axial stiffness values, torsional stiffness values, and hysteresis of 33 different categories and sizes of a commercially available passive-elastic prosthetic foot model, the Ossur low-profile (LP) Vari-flex with and without a shoe. We measured axial stiffness from compression and torsional stiffness from dorsiflexing and plantarflexing the prostheses. In general, a greater numerical prosthetic foot stiffness category resulted in increased heel, midfoot, and forefoot axial stiffness values, increased plantarflexion and dorsiflexion torsional stiffness values, and decreased heel, midfoot, and forefoot hysteresis. Moreover, a greater prosthetic foot size resulted in decreased heel, midfoot, and forefoot axial stiffness values, increased plantarflexion and dorsiflexion torsional stiffness values, and decreased heel and midfoot hysteresis. Finally, adding a shoe to the LP Vari-flex prosthetic foot resulted in decreased heel and midfoot axial stiffness values, decreased plantarflexion torsional stiffness values, and increased heel, midfoot, and forefoot hysteresis. In addition, we found that the force-displacement and torque-angle relationships were better described by curvilinear than linear equations. Ultimately, our results can be used to objectively compare LP Vari-flex prosthetic feet to other prosthetic feet in order to inform their prescription and design and use by people with a transtibial or transfemoral amputation.

bioengineering↗

bioRxiv2022

Evaluating the "cost of generating force" hypothesis across frequency in human running and hopping

The volume of active muscle and duration of extensor muscle force well-explain the associated metabolic energy expenditure across body mass and speed during level-ground running and hopping. However, if these parameters fundamentally drive metabolic energy expenditure, then they should pertain to multiple modes of locomotion and provide a simple framework for relating biomechanics to metabolic energy expenditure in bouncing gaits. Therefore, we evaluated the ability of the cost of generating force hypothesis to link biomechanics and metabolic energy expenditure during human running and hopping across step frequencies. We asked participants to run and hop at 0%, {+/-}8% and {+/-}15% of preferred step frequency. We calculated changes in active muscle volume, force duration, and metabolic energy expenditure. Overall, as step frequency increased, active muscle volume decreased due to postural changes via effective mechanical advantage (EMA) or duty factor. Accounting for changes in EMA and muscle volume better related to metabolic energy expenditure during running and hopping at different step frequencies than assuming a constant EMA and muscle volume. Thus, to ultimately develop muscle mechanics models that can explain metabolic energy expenditure across different modes of locomotion, we suggest more precise measures of muscle force production that include the effects of EMA.

physiology↗

bioRxiv2021

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

Ground reaction forces (GRFs) are important for understanding human movement, but their measurement is generally limited to a laboratory. Previous studies used neural networks to predict GRF waveforms during running from wearable device data, but these predictions are limited to the stance phase of level-ground running. We sought to develop a recurrent neural network capable of predicting continuous normal (perpendicular to surface) GRFs across a range of running speeds and slopes from accelerometer data. 19 subjects ran on a force-measuring treadmill at five slopes (0{degrees}, {+/-}5{degrees}, {+/-}10{degrees}) and three speeds (2.5, 3.33, 4.17 m/s) per slope with sacral- and shoe-mounted accelerometers. We then trained a recurrent neural network to predict normal GRF waveforms frame-by-frame. The predicted versus measured GRF waveforms had an average {+/-} SD RMSE of 0.16 {+/-} 0.04 BW and relative RMSE of 6.4 {+/-} 1.5% across all conditions and subjects. The recurrent neural network predicted continuous normal GRF waveforms across a range of running speeds and slopes with greater accuracy than neural networks implemented in previous studies. This approach may facilitate predictions of biomechanical variables outside the laboratory in near real-time and improves the accurately of quantifying and monitoring external loads experienced by the body when running.

bioengineering↗