Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

Abstract

Proportional control using surface electromyography (EMG) enables more intuitive control of a transfemoral prosthesis. However, EMG is a noisy signal which can vary over time, giving rise to the question what approach for knee torque estimation is most suitable for multi-day control. In this study we compared three different modelling frameworks to estimate knee torque in non-weight-bearing situations. The first model contained a convolutional neural network (CNN) which mapped EMG to knee torque directly. The second used a neuromusculoskeletal model (NMS) which used EMG, muscle tendon unit lengths and moment arms to compute knee torque. The third model (Hybrid) used a CNN to map EMG to specific muscle activation, which was used together with NMS components to compute knee torque. Multi-day measurements were conducted on ten able-bodied participants who performed non-weight bearing activities. CNN had the best performance in general and on each day (Normalized Root Mean Squared Error (NRMSE) 9.2 ± 4.4%). The Hybrid model (NRMSE 12.4 ± 3.4%) was able to outperform NMS (NRMSE 14.3 ± 4.2%). The NMS model showed no significant difference between measurement days. The CNN model and Hybrid models had significant performance differences between the first day and all other days. CNNs are suited for multi-day torque estimation in terms of error rate, outperforming the other two model types. NMS was the only model type which was robust over all days. This study investigated the behavior of three model types over multiple days, giving insight in the most suited modelling approach for multi-day torque estimation to be used in prosthetic control.

Design, Kinematics and Gait Analysis, of Prosthetic Knee Joints: A Systematic Review

“…Research work for transfemoral prosthetic knee joint led to the point that volitional control and surface recognition are interlinked, as the variation in the surface would make amputees make accordingly decisions [54][55][56][57][58][59]. Different algorithms are deployed for surface electromyography (sEMG), for instance, recurrent neural network (RNN), convolutional neural network (CNN), support vector machine (SVM), etc.…”

“…A single-channel EMG signal-based surface identification method uses a single classifier [54]. Their results could be promising in the future, if tested in a real-time scenario, rather than a virtual environment [55][56][57]. Similarly, some EMG-based algorithms are tested offline, rather than with actual prostheses and amputees [55].…”

“…Their results could be promising in the future, if tested in a real-time scenario, rather than a virtual environment [55][56][57]. Similarly, some EMG-based algorithms are tested offline, rather than with actual prostheses and amputees [55]. They were worn by able-bodied subjects, to capture EMG signals and testing.…”

EMG-Based Estimation of Lower Limb Joint Angles and Moments Using Long Short-Term Memory Network

“…There is a recent trend of combining EMG-driven muscle modeling and neural networks. The hybrid model used a CNN to map sEMG to specific muscle activation, which was used together with neuromusculoskeletal model components to compute knee torque [ 24 ]. A tradeoff exists between muscle model dependency and data-driven neural networks [ 25 ].…”

Consistent control information driven musculoskeletal model for multiday myoelectric control

“…Further, Pan et al developed a user-generic musculoskeletal model for continuous prediction of coordinated movements of hand and wrist [21][22][23]. Compared with data-driven approaches, the MMbased controller can provide such system robustness owing to three aspects: (1) it mimics human's physiological movements by decoding explicit representations of anatomical structures of the neuromusculoskeletal system [24,25]; (2) any muscletendon parameters of MMs are constrained within the operational space of the human's neuromusculoskeletal system [18]; (3) insights into the underlying motion generation process of biomechanical system have been disclosed through biomechanical modelling [26].…”