Exploring Tactile Temporal Features for Object Pose Estimation during Robotic Manipulation.

Dexterous robotic manipulation tasks depend on estimating the state of in-hand objects, particularly their orientation. Although cameras have been traditionally used to estimate the object's pose, tactile sensors have recently been studied due to their robustness against occlusions. This paper explores tactile data's temporal information for estimating the orientation of grasped objects.

Feature-Classifier Pairing Compatibility for sEMG Signals in Hand Gesture Recognition under Joint Effects of Processing Procedures.

Gesture recognition using surface electromyography (sEMG) serves many applications, from human-machine interfaces to prosthesis control. Many features have been adopted to enhance recognition accuracy. However, studies mostly compare features under a prechosen feature window size or a classifier, biased to a specific application.

A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance.

ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers' performance.