Abstract
InverseMuscleNET, a machine learning model, is proposed as an alternative to static optimization for resolving the redundancy issue in inverse muscle models. A recurrent neural network (RNN) was optimally configured, trained, and tested to estimate the pattern of muscle activation signals. Five biomechanical variables (joint angle, joint velocity, joint acceleration, joint torque, and activation torque) were used as inputs to the RNN. A set of surface electromyography (EMG) signals, experimentally measured around the shoulder joint for flexion/extension, were used to train and validate the RNN model. The obtained machine learning model yields a normalized regression in the range of 88–91% between experimental data and estimated muscle activation. A sequential backward selection algorithm was used as a sensitivity analysis to discover the less dominant inputs. The order of most essential signals to least dominant ones was as follows: joint angle, activation torque, joint torque, joint velocity, and joint acceleration. The RNN model required 0.06 s of the previous biomechanical input signals and 0.01 s of the predicted feedback EMG signals, demonstrating the dynamic temporal relationships of the muscle activation profiles. The proposed approach permits a fast and direct estimation ability instead of iterative solutions for the inverse muscle model. It raises the possibility of integrating such a model in a real-time device for functional rehabilitation and sports evaluation devices with real-time estimation and tracking. This method provides clinicians with a means of estimating EMG activity without an invasive electrode setup.
Highlights
Muscle contractions generate tension and, as a result, a moment about the human joint
The optimum configuration of the recurrent neural network (RNN) model, the analysis of dominant input signals, and the estimation possibility of the EMG signals are presented
The mapping of biomechanical signals to EMG signals should be limited to a linear function
Summary
Muscle contractions generate tension and, as a result, a moment about the human joint. Knowing the muscle forces or activation signals during human movement can assist in understanding the underlying biomechanical systems (Crowninshield and Brand, 1981). These analyses can improve movement performance, especially for athletes and patients (Laschowski et al, 2018). First, the raw EMG goes through initial filtering steps and a muscle activation dynamic model (Manal and Buchanan, 2003; Desplenter and Trejos, 2018). The resultant activation converts to muscle forces using muscle contraction dynamics (Heitmann et al, 2012; Heidlauf and Rohrle, 2014; Desplenter and Trejos, 2018). The muscle forces convert to joint torque using the musculoskeletal geometry
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