Load Carriage During Walking Increases Dynamic Stiffness at Distal Lower Limb Joints

The addition of a load during walking requires changes in the movement pattern. The investigation of the dynamic joint stiffness behavior may help to understand the lower limb joints’ contribution to these changes. This study aimed to investigate the dynamic stiffness of lower limb joints in response to the increased load carried while walking. Thirteen participants walked in two conditions: unloaded (an empty backpack) and loaded (the same backpack plus added mass corresponding to 30% of body mass). Dynamic stiffness was calculated as the linear slope of the regression line on the moment–angle curve during the power absorption phases of the ankle, knee, and hip in the sagittal plane. The results showed that ankle (P = .002) and knee (P < .001) increased their dynamic stiffness during loaded walking compared with unloaded, but no difference was observed at the hip (P = .332). The dynamic stiffness changes were different among joints (P < .001): ankle and knee changes were not different (P < .992), but they had a greater change than hip (P < .001). The nonuniform increases in lower limb joint dynamic stiffness suggest that the ankle and knee are critical joints to deal with the extra loading.

Energetics of Walking With a Robotic Knee Exoskeleton

The authors tested 4 young healthy subjects walking with a powered knee exoskeleton to determine if it could reduce the metabolic cost of locomotion. Subjects walked with a backpack loaded and unloaded, on a treadmill with inclinations of 0° and 15°, and outdoors with varied natural terrain. Participants walked at a self-selected speed (average 1.0 m/s) for all conditions, except incline treadmill walking (average 0.5 m/s). The authors hypothesized that the knee exoskeleton would reduce the metabolic cost of walking uphill and with a load compared with walking without the exoskeleton. The knee exoskeleton reduced metabolic cost by 4.2% in the 15° incline with the backpack load. All other conditions had an increase in metabolic cost when using the knee exoskeleton compared with not using the exoskeleton. There was more variation in metabolic cost over the outdoor walking course with the knee exoskeleton than without it. Our findings indicate that powered assistance at the knee is more likely to decrease the metabolic cost of walking in uphill conditions and during loaded walking rather than in level conditions without a backpack load. Differences in positive mechanical work demand at the knee for varying conditions may explain the differences in metabolic benefit from the exoskeleton.

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.MethodsWe 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.ResultsThe 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.ConclusionsOur 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.