Grasp Classification With Weft Knit Data Glove Using a Convolutional Neural Network

Grasp classification using data gloves can enable therapists to monitor patients efficiently by providing concise information about the activities performed these patients. Although, classical machine learning algorithms have been applied in grasp classification, they require manual feature extraction achieve high accuracy. In contrast, convolutional neural networks (CNNs) outperformed popular several scenarios because of their ability extract features automatically from raw data. However, not implemented on a glove. this study, we apply CNN piezoresistive textile glove knitted conductive yarn and an elastomeric yarn. The was used collect five participants who grasped thirty objects each following Schlesinger's taxonomy. We investigate CNN's performance two where validation are known unknown. Our results show that simple architecture k-nn, Gaussian SVM, Decision Tree both terms