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1. Integration of EMG-Based Learning and Sliding Mode Control for an Exoskeleton Assist-as-Needed Support System

   https://doi.org/10.1115/DETC2022-91305  

   Delgado, Pablo; Gonzalez, Nathan; Yihun, Yimesker (August 2022, American Society of Mechanical Engineers)

   Abstract In this study, an electromyography (EMG) signal-based learning is integrated with a Sliding-Mode Control (SMC) law for an effective human-exoskeleton synergy. A modified Recursive Newton-Euler Algorithm (RNEA) with SMC was used to determine and control the inverse dynamics of a highly nonlinear 4 degree-of-freedom exoskeleton designed for the automation of upper-limp therapeutic exercises. The exoskeleton position and velocity, along with the raw EMG signal from the bicep Brachii muscle were used as a feedback. The root mean square (RMS) values of targeted muscles EMG are tracked in a predetermined time window to quantify an adaptive threshold value and control the torque at the exoskeleton joints accordingly. Simulations of the proposed robust control law have been done in MATLAB-Simulink. Results have shown that the designed hybrid Control strategy offers the ability to adjust the needed support instantly based on the amount of muscle engagement presented in the combined motion of the human-exoskeleton system while maintaining the state trajectory errors and input torque bounded to ±7 × 10−3 rads and ±5 N.m, respectively. 

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2. Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds

   https://doi.org/10.1186/s12984-022-01061-z  

   Zhang, Qiang; Fragnito, Natalie; Franz, Jason R.; Sharma, Nitin (August 2022, Journal of NeuroEngineering and Rehabilitation)

   Abstract BackgroundImproving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI’s prediction accuracy. ObjectiveThe purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging. MethodsTen young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds. ResultsOn average, the normalized moment prediction root mean square error was reduced by 14.58 % ($$p=0.012$$ $p=0.012$) and 36.79 % ($$p<0.001$$ $p<0.001$) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction. ConclusionsThe proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction. 

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3. Deep Neural Network Based Saturated Adaptive Control of Muscles in a Lower-Limb Hybrid Exoskeleton

   https://doi.org/10.1115/IMECE2023-112415  

   Ting, Jonathan A; Basyal, Sujata; Allen, Brendon C (October 2023, American Society of Mechanical Engineers)

   Hybrid exoskeletons are used to blend the rehabilitative efficacy and mitigate the shortcomings of functional electrical stimulation (FES) and exoskeleton-based rehabilitative solutions. This paper introduces a novel nonlinear controller that may potentially improve the rehabilitative efficiency of a lower limb hybrid exoskeleton by implementing four key features into the FES and exoskeleton controllers. First, the FES input to the user’s muscles is saturated based on user preference to ensure user comfort. Second, rather than discarding the excess control effort from the saturated FES input, it is redirected into the exoskeleton’s motor controller. Third, a safe deep neural network (DNN) is designed to estimate the unknown dynamics of the hybrid exoskeleton and the DNN is implemented in the FES controller to improve the control efficiency and tracking performance. Fourth, an adaptive update law is designed to estimate the unknown muscle control effectiveness to facilitate the implementation of the DNN. Lyapunov stability-based methods are used to generate real-time adaptive update laws that will train the adaptive estimate of the muscle effectiveness and the output layer weights of the DNN in real-time, ensure the effectiveness and safety of the controllers, and prove global asymptotic tracking of the desired trajectory. 

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4. A Novel Control Law for Multi-joint Human-Robot Interaction Tasks While Maintaining Postural Coordination

   Keya Ghonasgi; Reuth Mirsky; Adrian M. Haith; Peter Stone; Ashish D. Deshpande (October 2023, IEEE)

   Exoskeleton robots are capable of safe torque- controlled interactions with a wearer while moving their limbs through pre-defined trajectories. However, affecting and assist- ing the wearer’s movements while incorporating their inputs (effort and movements) effectively during an interaction re- mains an open problem due to the complex and variable nature of human motion. In this paper, we present a control algorithm that leverages task-specific movement behaviors to control robot torques during unstructured interactions by implementing a force field that imposes a desired joint angle coordination behavior. This control law, built by using principal component analysis (PCA), is implemented and tested with the Harmony exoskeleton. We show that the proposed control law is versatile enough to allow for the imposition of different coordination behaviors with varying levels of impedance stiffness. We also test the feasibility of our method for unstructured human-robot interaction. Specifically, we demonstrate that participants in a human-subject experiment are able to effectively perform reaching tasks while the exoskeleton imposes the desired joint coordination under different movement speeds and interaction modes. Survey results further suggest that the proposed control law may offer a reduction in cognitive or motor effort. This control law opens up the possibility of using the exoskeleton for training the participating in accomplishing complex m 

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5. 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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