1D-Convolutional Neural Networks can Quantify Therapy Content of Children and Adolescents Walking in a Robot-Assisted Gait Trainer.

Abstract

Therapy content, consisting of device parameter settings and therapy instructions, is crucial for an effective robot-assisted gait therapy program. Settings and instructions depend on the therapy goals of the individual patient. While device parameters can be recorded by the robot, therapeutic instructions and associated patient responses are currently difficult to capture. This limits the transferability of successful therapeutic approaches between clinics. Here, we propose that 1D-convolutional neural networks can be used to relate patient behavior during individual steps to the instructions given as a surrogate for the patient's intent. Our model takes the surface electromyography patterns of two leg muscles as input and predicts the given instruction as output. We tested this approach with data from 20 healthy children walking in a robot-assisted gait trainer with 5 different instructions. Our model performs well, with a classification accuracy of almost 90%, when the instruction targets specific aspects of gait, such as step length. This shows that 1D-convolutional neural networks are a viable tool for quantifying therapy content. Thus, they could help compare therapy approaches and identify effective strategies.