Back

Frontal hip exoskeleton assistance does not appear promising for reducing the metabolic cost of walking: A preliminary experimental study

Kim, J.; Raitor, M.; Liu, C. K.; Collins, S. H.

2023-08-23 bioengineering
10.1101/2023.08.22.554212 bioRxiv
Show abstract

BackgroundDuring walking, humans exert a substantial hip abduction moment to maintain balance and prevent pelvic drop. This significant torque requirement suggests that assisting the frontal hip muscles could be a promising strategy to reduce the energy expenditure associated with walking. A previous musculoskeletal simulation study also predicted that providing hip abduction assistance through an exoskeleton could potentially result in a large reduction in whole-body metabolic rate. However, to date, no study has experimentally assessed the metabolic cost of walking with frontal hip assistance. MethodsIn this case study involving a single subject (N = 1), a tethered hip exoskeleton emulator was used to assess the feasibility of reducing metabolic expenditure through frontal-plane hip assistance. Human-in-the-loop optimization was conducted separately under torque and position control to determine energetically optimal assistance parameters for each control scheme. ResultsThe optimized profiles in both control schemes did not reduce metabolic rate compared to walking with assistance turned off. The optimal peak torque magnitude was found to be close to zero, suggesting that any hip abduction torque would increase metabolic rate. Both bio-inspired and simulation-inspired profiles substantially increased metabolic cost. ConclusionFrontal hip assistance does not appear to be promising in reducing the metabolic rate of walking. This could be attributed to the need for maintaining balance, as humans may refrain from relaxing certain muscles as a precaution against unexpected disturbances during walking. An investigation of different control architectures is needed to determine if frontal-plane hip assistance can yield successful results.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.