Comparing classification techniques for identification of grasped objects

BACKGROUND: This work presents a comparison and selection of different machine learning classification techniques applied in the identification of objects using data collected by an instrumented glove during a grasp process. The selected classifiers techniques can be applied to e-rehabilitation and e-training exercises for different pathologies, as in aphasic patients. METHODS: The adopted method uses the data from a commercial instrumented glove. An experiment was carried out, where three subjects using an instrumented glove had to grasp eight objects of common use. The collected data were submitted to nineteen different classification techniques (available on the scikit-learn library of Python) used in two classifier structures, with the objective of identifying the grasped object. The data were organized into two dataset scenarios: one with data from the three users and another with individual data. RESULTS: As a result of this work, three classification techniques presented similar accuracies for the classification of objects. Also, it was identified that when training the models with individual dataset the accuracy improves from 96 to 99%. CONCLUSIONS: Classification techniques were used in two classifier structures, one based on a single model and the other on a cascade model. For both classifier structure and scenarios, three of the classification techniques were selected due to the high reached accuracies. The highest results were obtained using the classifier structure that employed the cascade models and the scenario of individual dataset.