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1. Neuromechanics and Energetics of Walking With an Ankle Exoskeleton Using Neuromuscular-Model Based Control: A Parameter Study

   https://doi.org/10.3389/fbioe.2021.615358  

   Shafer, Benjamin A.; Philius, Sasha A.; Nuckols, Richard W.; McCall, James; Young, Aaron J.; Sawicki, Gregory S. (April 2021, Frontiers in Bioengineering and Biotechnology)

   null (Ed.)

   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. 

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2. Design and Evaluation of a Bilateral Semi-Rigid Exoskeleton to Assist Hip Motion

   https://doi.org/10.3390/biomimetics9040211  

   Mohammadzadeh_Gonabadi, Arash; Antonellis, Prokopios; Dzewaltowski, Alex C; Myers, Sara A; Pipinos, Iraklis I; Malcolm, Philippe (April 2024, Biomimetics)

   This study focused on designing and evaluating a bilateral semi-rigid hip exoskeleton. The exoskeleton assisted the hip joint, capitalizing on its proximity to the body’s center of mass. Unlike its rigid counterparts, the semi-rigid design permitted greater freedom of movement. A temporal force-tracking controller allowed us to prescribe torque profiles during walking. We ensured high accuracy by tuning control parameters and series elasticity. The evaluation involved experiments with ten participants across ten force profile conditions with different end-timings and peak magnitudes. Our findings revealed a trend of greater reductions in metabolic cost with assistance provided at later timings in stride and at greater magnitudes. Compared to walking with the exoskeleton powered off, the largest reduction in metabolic cost was 9.1%. This was achieved when providing assistance using an end-timing at 44.6% of the stride cycle and a peak magnitude of 0.11 Nm kg−1. None of the tested conditions reduced the metabolic cost compared to walking without the exoskeleton, highlighting the necessity for further enhancements, such as a lighter and more form-fitting design. The optimal end-timing aligns with findings from other soft hip exosuit devices, indicating a comparable interaction with this prototype to that observed in entirely soft exosuit prototypes. 

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3. Estimating human joint moments unifies exoskeleton control, reducing user effort

   https://doi.org/10.1126/scirobotics.adi8852  

   Molinaro, Dean D; Kang, Inseung; Young, Aaron J (March 2024, Science Robotics)

   Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community. 

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4. User and Environmental Context Adaptive Knee Exoskeleton Assistance using Electromyography

   https://doi.org/10.1109/ISMR57123.2023.10130260  

   Lee, Dawit; Kang, Inseung; Kogler, Géza F.; Hammond, Frank L.; Young, Aaron J. (April 2023, IEEE 2023 International Symposium on Medical Robotics (ISMR))

   Proportional myoelectric controller (PMC) has been one of the most common assistance strategies for robotic exoskeletons due to its ability to modulate assistance level directly based on the user's muscle activation. However, existing PMC strategies (static or user-adaptive) scale torque linearly with muscle activation level and fail to address complex and non-linear mapping between muscle activation and joint torque. Furthermore, previously presented adaptive PMC strategies do not allow for environmental changes (such as changes in ground slopes) and modulate the system's assistance level over many steps. In this work, we designed a novel user- and environment-adaptive PMC for a knee exoskeleton that modulates the peak assistance level based on the slope level during locomotion. We recruited nine able-bodied adults to test and compare the effects of three different PMC strategies (static, user-adaptive, and user- and environment-adaptive) on the user's metabolic cost and the knee extensor muscle activation level during load-carriage walking (6.8 kg) in three inclination settings (0°, 4.5°, and 8.5°). The results showed that only the user- and environment-adaptive PMC was effective in significantly reducing user's metabolic cost (5.8% reduction) and the knee extensor muscle activation (19% reduction) during 8.5° incline walking compared to the unpowered condition while other PMCs did not have as large of an effect. This control framework highlights the viability of implementing an assistance paradigm that can dynamically adjust to the user's biological demand, allowing for a more personalized assistance paradigm. 

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5. Electromyography (EMG) Signal Contributions in Speed and Slope Estimation Using Robotic Exoskeletons

   https://doi.org/10.1109/ICORR.2019.8779433  

   Kang, Inseung; Kunapuli, Pratik; Hsu, Hsiang; Young, Aaron J. (June 2019, 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR))

   Robotic exoskeletons have the capability to improve community ambulation in aging individuals. These exoskeleton controllers utilize different environmental information such as walking speeds and slope inclines to provide corresponding assistance. Several numerical approaches for estimating this environmental information have been implemented; however, they tend to be limited during dynamic changes. A possible solution is a machine learning model utilizing the user's electromyography (EMG) signals along with mechanical sensor data. We developed a neural network-based walking speed and slope estimator for a powered hip exoskeleton and explored the EMG signal contributions in both static and dynamic settings while wearing the device. We also analyzed the performance of different EMG electrode placements. The resulting machine learning model achieved error rates below 0.08 m/s RMSE and 1.3 RMSE. Our study findings from four able-bodied and two elderly subjects indicate that EMG can improve the performance by reducing the error rate by 14.8% compared to the model using only mechanical sensors. Additionally, results show that using EMG electrode configuration within the exoskeleton interface region is sufficient for the EMG model performance. 

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