Robust Torque Predictions From Electromyography Across Multiple Levels of Active Exoskeleton Assistance Despite Non-linear Reorganization of Locomotor Output

Abstract

Robotic exoskeletons can assist humans with walking by providing supplemental torque in proportion to the user's joint torque. Electromyographic (EMG) control algorithms can estimate a user's joint torque directly using real-time EMG recordings from the muscles that generate the torque. However, EMG signals change as a result of supplemental torque from an exoskeleton, resulting in unreliable estimates of the user's joint torque during active exoskeleton assistance. Here, we present an EMG control framework for robotic exoskeletons that provides consistent joint torque predictions across varying levels of assistance. Experiments with three healthy human participants showed that using diverse training data (from different levels of assistance) enables robust torque predictions, and that a convolutional neural network (CNN), but not a Kalman filter (KF), can capture the non-linear transformations in EMG due to exoskeleton assistance. With diverse training, the CNN could reliably predict joint torque from EMG during zero, low, medium, and high levels of exoskeleton assistance [root mean squared error (RMSE) below 0.096 N-m/kg]. In contrast, without diverse training, RMSE of the CNN ranged from 0.106 to 0.144 N-m/kg. RMSE of the KF ranged from 0.137 to 0.182 N-m/kg without diverse training, and did not improve with diverse training. When participant time is limited, training data should emphasize the highest levels of assistance first and utilize at least 35 full gait cycles for the CNN. The results presented here constitute an important step toward adaptive and robust human augmentation via robotic exoskeletons. This work also highlights the non-linear reorganization of locomotor output when using assistive exoskeletons; significant reductions in EMG activity were observed for the soleus and gastrocnemius, and a significant increase in EMG activity was observed for the erector spinae. Control algorithms that can accommodate spatiotemporal changes in muscle activity have broad implications for exoskeleton-based assistance and rehabilitation following neuromuscular injury.

Influence of Input Features and EMG Type on Ankle Joint Torque Prediction With Support Vector Regression

“…In this study, only 12 repetitions of each movement were used for training in the intra-session case, while other studies using CNN for torque prediction reported using 16 or more repetitions for training. For instance, George et al [5] reported that at least 20 gait cycles were needed for the CNN prediction (intra-session) of hip sagittal plane joint torque to start improving, and the results only became reliable at around 35 gait cycles. Schulte et al [11] achieved good CNN prediction of the knee non-weight bearing torque over several days using the trial of 20 repetitions with a 80% training and 20% validation split.…”

“…It allows detecting the movement before the onset [1] thus improving the device acceptance by the users [2], [3]. Moreover, this approach allows incorporating the active effort of the user [4], [5] even in cases when the movement is altered due to musculoskeletal impairment [6]. Accurately mapping the EMG signals of the muscles surrounding the joint to the joint torque is not trivial due to the non-linear relationship between these variables.…”

Coordination Between Partial Robotic Exoskeletons and Human Gait: A Comprehensive Review on Control Strategies

“…These metrics would unify the validation process of robotic exoskeletons and would justify the scientific evidence and clinical relevance of their role in gait assistance or rehabilitation. In this regard, understanding and estimating the torque and force transmission from the robotic device to the user and the biomechanical effects of this interaction is crucial, as it could boost the therapeutic outcomes involving exoskeletons in rehabilitation therapies ( Lerner et al, 2017a ; George et al, 2021 ). These methods would enable the comparison between systems as they directly quantify the exoskeleton performance and establish a common paradigm that can be used independently of the evaluated device.…”

Powered Hip Exoskeleton Reduces Residual Hip Effort Without Affecting Kinematics and Balance in Individuals With Above-Knee Amputations During Walking

“…Also, we observed a reduction in residual limb extension torque, which could indicate reduced effort in the extensor muscles in the residual limb. Future work should explore the changes in lower-limb muscular effort while wearing the hip exoskeleton, similar to what was recently done in nonamputee individuals [50].…”