https://dx.doi.org/10.3390/s19163548

In this paper, we present a putEMG dataset intended for the 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. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). (more…)

putEMG & putEMG-Force datasets are databases of sEMG activity recorded from forearm. Experiment was conducted on 45 participants, twice for each one of them. Datasets includes 7 active gestures (like hand flexion, extension etc.) + idle and a set of trials with isometric contractions. Matrix of 24 electrodes was used.

The putEMG datasets are available free of charge under Creative Commons license. We encourage you to utilize putEMG datasets and share the results.

Description and data repository is available here: putEMG datasets

https://doi.org/10.1109/MFI.2016.7849546

In this article, a method for kinematic configuration estimation of a structure similar to a human finger, is presented. The method is based on the EKF and a model reflecting kinematic constraints of a finger-like structure (2-DOF metacarpophalangeal joint, and one 1-DOF proximal interphalangeal rotational joint), using 3 low cost IMUs. During tests, the IMUs were attached to a 3D-printed setup equipped with encoders. (more…)

https://doi.org/10.23919/SPA.2017.8166836

This paper presents a method for radial shift estimation of an electrode array located around the forearm. The algorithm is aimed at band-shaped EMG human-machine interfaces recognising hand gestures. Proposed algorithm relies on the approximation of muscle activity in several regions arranged radially around user’s forearm. The intensity is represented as a polygon on a polar plane. To estimate current electrode band orientation, the user is asked to perform a certain gesture. (more…)

https://doi.org/10.23919/SPA.2017.8166835

This paper presents a study of a human-machine interface in the form of three parallel electromyographic bands placed around the user’s forearm, with the influence of sEMG electrode layout on gesture recognition performance as the primary focus. Tested electrode configurations included setups ranging from 4 to 24 electrodes, with varying placement on the subject’s forearm, using both monopolar and bipolar measurement methods. An artificial neural network with softmax output layer was used as the gesture classifier. The test data included three participants performing nine gestures in various sequences, over the course of two days. (more…)