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Delp, S. L.

Publications and source records attributed to Delp, S. L..

20 records · Page 2Linked to original sources

Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations

Deficits in the ankle plantarflexor muscles, such as weakness and contracture, occur commonly in conditions such as cerebral palsy, stroke, muscular dystrophy, and Charcot-Marie-Tooth disease. While these deficits likely contribute to observed gait pathologies, elucidating cause-effect relationships is difficult due to the often co-occurring biomechanical and neural deficits. To elucidate the effects of weakness and contracture, we systematically introduced isolated deficits into a musculoskeletal model and generated simulations of walking to predict gait adaptations due to these deficits. We developed a planar model containing 9 degrees of freedom and 18 musculotendon actuators, and an optimization framework through which we imposed simple objectives, such as minimizing cost of transport while avoiding falling and injury, and maintaining head stability. We first validated that our model could generate gaits that reproduced experimentally observed kinematic, kinetic, and metabolic trends for two cases: 1) walking at prescribed speeds between 0.50 m/s and 2.00 m/s and 2) walking at self-selected speed. We then applied mild, moderate, and severe levels of muscle weakness or contracture to either the soleus (SOL) or gastrocnemius (GAS) or both of these major plantarflexors (PF) and retrained the model to walk at a self-selected speed. The model was robust to all deficits, finding a stable gait in all cases. Severe PF weakness caused the model to adopt a slower, \"heel-walking\" gait. Severe contracture of only SOL or both PF yielded similar results: the model adopted a \"toe-walking\" gait with excessive hip and knee flexion during stance. These results highlight how plantarflexor weakness and contracture may contribute to observed gait patterns. Our model, simulation and optimization framework, and results are freely shared so that others can reproduce and build upon our work.\n\nAuthor summaryDeficits in the muscles that extend the ankle are thought to contribute to abnormal walking patterns in conditions such as cerebral palsy, stroke, muscular dystrophy, and Charcot-Marie-Tooth disease. To study how deficits in these muscles contribute to abnormal walking patterns, we used computer simulations to systematically introduce muscle deficits into a biomechanically accurate model. We first showed that our model could discover realistic walking patterns over a wide range of speeds when we posed a simple objective: walking while consuming a minimum amount of energy per distance, maintaining head stability, and avoiding injury. We then used the model to study the effect of two commonly observed problems: muscle weakness and muscle tightness. We found that severe weakness of the ankle extensors caused the model to adopt a slower, \"heel-walking\" gait, and severe tightness caused the model to adopt a crouched, \"toe-walking\" gait. These results highlight how deficits in the ankle extensor muscles may contribute to abnormal walking patterns commonly seen in pathological populations.

bioengineering↗

Rapid energy expenditure estimation for assisted and inclined loaded walking

BackgroundEstimating energy expenditure with indirect calorimetry requires expensive equipment and provides slow and noisy measurements. Rapid estimates using wearable sensors would enable techniques like optimizing assistive devices outside a lab. Existing methods correlate data from wearable sensors to measured energy expenditure without evaluating the accuracy of the estimated energy expenditure for activity conditions or subjects not included in the correlation process. Our goal is to assess data-driven models that are capable of rapidly estimating energy expenditure for new conditions and subjects.\n\nMethodsWe developed models that estimated energy expenditure from two datasets during walking conditions with (1) ankle exoskeleton assistance and (2) various loads and inclines. The estimation was portable and rapid, using input features that are possible to measure with wearable sensors and restricting the input data length to a single gait cycle or four second interval. The performance of the models was evaluated for three use cases. The first case estimated energy expenditure during walking conditions for subjects with some subject specific training data available. The second case estimated all conditions in the dataset for a new subject not included in the training data. The third case estimated new conditions for a new subject. The models also ordered the magnitude of energy expenditure across all conditions for a new subject.\n\nResultsThe average errors in energy expenditure estimation during assisted walking conditions were 4.4%, 8.0%, and 8.1% for the three use cases, respectively. The average errors in energy expenditure estimation during inclined and loaded walking conditions were 6.1%, 9.7%, and 11.7% for the three use cases. The models ordered the magnitude of energy expenditure with a maximum and average percentage of correctly ordered conditions of 56% and 43% for assisted walking and 85% and 55% for incline and loaded walking.\n\nConclusionsOur data-driven models determined the accuracy of energy expenditure estimation for three use cases. For experiments where the accuracy of a data-driven model is sufficient, standard indirect calorimetry can be replaced. The energy expenditure ordering could aid in selecting optimal assistance conditions. The models, code, and datasets are provided for reproduction and extension of our results.

bioengineering↗