2 research outputs found
putEMG -- a surface electromyography hand gesture recognition dataset
In this paper, we present a putEMG dataset intended for evaluation of hand
gesture recognition methods based on sEMG signal. The dataset was acquired for
44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4
pinches, and idle). It consists of uninterrupted recordings of 24 sEMG channels
from the subject's forearm, RGB video stream and depth camera images used for
hand motion tracking. Moreover, exemplary processing scripts are also
published. putEMG dataset is available under Creative Commons
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license at:
https://www.biolab.put.poznan.pl/putemg-dataset/. The dataset was validated
regarding sEMG amplitudes and gesture recognition performance. The
classification was performed using state-of-the-art classifiers and feature
sets. Accuracy of 90% was achieved for SVM classifier utilising RMS feature and
for LDA classifier using Hudgin's and Du's feature sets. Analysis of
performance for particular gestures showed that LDA/Du combination has
significantly higher accuracy for full hand gestures, while SVM/RMS performs
better for pinch gestures. Presented dataset can be used as a benchmark for
various classification methods, evaluation of electrode localisation concepts,
or development of classification methods invariant to user-specific features or
electrode displacement
PUT-HandâHybrid Industrial and Biomimetic Gripper for Elastic Object Manipulation
In this article, the design of a five-fingered anthropomorphic gripper is presented specifically designed for the manipulation of elastic objects. The manipulator features a hybrid design, being equipped with three fully actuated fingers for precise manipulation, and two underactuated, tendon-driven digits for secure power grasping. For ease of reproducibility, the design uses as many off-the-shelf and 3D-printed components as possible. The on-board controller circuit and firmware are also presented. The design includes resistive position and angle sensors in each joint, resulting in full joint observability. The controller has a position-based controller integrated, along with USB communication protocol, enabling gripper state reporting and direct motor control from a PC. A high-level driver operating as a Robot Operating System node is also provided. All drives and circuitry of the PUT-Hand are integrated within the hand itself. The sensory system of the hand includes tri-axial optical force sensors placed on fully actuated fingers’ fingertips for reaction force measurement. A set of experiments is provided to present the motion and perception capabilities of the gripper. All design files and source codes are available online under CC BY-NC 4.0 and MIT licenses