Muscle activation prediction in essential tremor through neuromusculoskeletal digital twinning and deep neural networks

Essential tremor (ET) is the most common movement disorder in adults, affecting up to 5% of the population over 65 years of age. Accurately predicting the dynamics of ET for each individual is crucial for optimizing therapies, such as sub-motor threshold stimulation (delivery of electrical currents below motoneuron activation), where the timing of stimulation is key for effective tremor reduction. Although there have been some efforts to implement machine learning predictive models, real-time prediction and estimation of muscle activation is still challenging due to the closed-loop nature of neuromuscular control, sensor noise, signal transmission delays, and scarcity of data. Here, we describe how a digital twin of ET—a computational neuromusculoskeletal model of ET deployed in SCONE simulator—allows for properly training deep recurrent neural networks (RNN) to predict muscle activation. Moreover, it permits parametrized synthetic simulation of the tremor. Results on predicting muscle activation from wrist flexo-extension movement show that the RNN has an average prediction accuracy of 81% and 83% with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) gated neurons, respectively3. While this work still uses only synthetic data, it shows the potential for treatment optimization and personalized therapeutic strategies, such as peripheral electrical stimulation.