Machine Learning Improves Use of Haptic Glove for Engineers in Virtual Reality

Abstract

Haptic gloves with force feedback represent new and immersive devices for Virtual Reality (VR). They enable interaction with virtual objects and have a positive impact on virtual engineering processes. The position of the hand and its specific finger positions, such as grip types, are tracked in virtual space during assembly processes. Implementing rule-based recognition of these grip types is complex and error prone due to hard- and software limitations. Machine Learning (ML) can support engineers during the use and implementation of these applications by classifying user input as specific grip types. Two ML algorithms, one Neural Network (NN) and one Support Vector Machine (SVM), that detect nine grip types at runtime by only using the joint angles of the glove’s exoskeleton as features, were developed and compared with a rule-based algorithm. Our research shows, that the ML algorithm reach a very high accuracy with only reading one feature compared to the rule-based algorithm.