Inclusive Human Intention Prediction with Wearable Sensors: Machine Learning Techniques for the Reaching Task Use Case †

Human intentions prediction is gaining importance with the increase of human-robot interaction challenges in several contexts, like industrial and clinical. This paper compares Linear Discriminant Analysis (LDA) and Random Forest (RF) performance in predicting the intention of moving towards a target during reaching movements, on ten subjects wearing four electromagnetic sensors. LDA and RF prediction accuracy is compared with respect to observation-sample dimension and noise presence, training and prediction time. Both algorithms achieved good accuracy, which improves as the sample dimension increases, although LDA presents better results for the current dataset.