Abstract
Powered ankle exoskeletons that apply assistive torques with optimized timing and magnitude can reduce metabolic cost by ∼10% compared to normal walking. However, finding individualized optimal control parameters is time consuming and must be done independently for different walking modes (e.g., speeds, slopes). Thus, there is a need for exoskeleton controllers that are capable of continuously adapting torque assistance in concert with changing locomotor demands. One option is to use a biologically inspired, model-based control scheme that can capture the adaptive behavior of the human plantarflexors during natural gait. Here, based on previously demonstrated success in a powered ankle-foot prosthesis, we developed an ankle exoskeleton controller that uses a neuromuscular model (NMM) comprised of a Hill type musculotendon driven by a simple positive force feedback reflex loop. To examine the effects of NMM reflex parameter settings on (i) ankle exoskeleton mechanical performance and (ii) users’ physiological response, we recruited nine healthy, young adults to walk on a treadmill at a fixed speed of 1.25 m/s while donning bilateral tethered robotic ankle exoskeletons. To quantify exoskeleton mechanics, we measured exoskeleton torque and power output across a range of NMM controller Gain (0.8–2.0) and Delay (10–40 ms) settings, as well as a High Gain/High Delay (2.0/40 ms) combination. To quantify users’ physiological response, we compared joint kinematics and kinetics, ankle muscle electromyography and metabolic rate between powered and unpowered/zero-torque conditions. Increasing NMM controller reflex Gain caused increases in average ankle exoskeleton torque and net power output, while increasing NMM controller reflex Delay caused a decrease in net ankle exoskeleton power output. Despite systematic reduction in users’ average biological ankle moment with exoskeleton mechanical assistance, we found no NMM controller Gain or Delay settings that yielded changes in metabolic rate. Post hoc analyses revealed weak association at best between exoskeleton and biological mechanics and changes in users’ metabolic rate. Instead, changes in users’ summed ankle joint muscle activity with powered assistance correlated with changes in their metabolic energy use, highlighting the potential to utilize muscle electromyography as a target for on-line optimization in next generation adaptive exoskeleton controllers.
Highlights
Lower-limb exoskeletons are a promising approach to reduce human effort by providing mechanical assistance to restore, replace, or augment the function of biological musculotendons during walking (Sawicki et al, 2020)
Users’ metabolic rate was unchanged when walking with powered ankle exoskeletons using neuromuscular model (NMM) based control across a range of parameter settings (Figure 4)
We developed an NMM comprised of a Hill-type musculotendon driven by a simple positive force feedback reflex loop and examined the effects of the NMM reflex Gain and Delay settings on (i) ankle exoskeleton mechanical performance and (ii) users’ physiological response
Summary
Lower-limb exoskeletons are a promising approach to reduce human effort by providing mechanical assistance to restore, replace, or augment the function of biological musculotendons during walking (Sawicki et al, 2020). Based on the large contribution of ankle musculotendons to the overall mechanical power generated by the lower-limb during walking (Farris and Sawicki, 2012), researchers and engineers have focused heavily on delivering power with ankle exoskeletons as a means for reducing metabolic cost of walking (Sawicki and Ferris, 2008; Malcolm et al, 2013; Mooney et al, 2014; Jackson and Collins, 2015; Galle et al, 2017; Zhang et al, 2017b; Grimmer et al, 2019). Using an adaptive controller that does not need to be tuned for each mode, optimally once per individual, could increase user acceptance of robotic exoskeletons for everyday use in dynamic environments
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