Category: News

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). Continue reading putEMG—A Surface Electromyography Hand Gesture Recognition Dataset

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. Continue reading EKF-based method for kinematic configuration estimation of finger-like structure using low grade multi-IMU system

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. Continue reading Localisation method for sEMG electrode array, towards hand gesture recognition HMI development

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. Continue reading Influence of sEMG electrode matrix configuration on hand gesture recognition performance

https://doi.org/10.23919/SPA.2017.8166837

This paper presents a study on the feasibility of an interface based on a mechanomyographic signal (MMG). Existing state-of-the-art studies show attempts of utilisation of MMG signal for gesture recognition where the sensors’ location is strictly defined with respect to muscle position. A test setup consisting of 5 IMU sensors arranged in a band form was used. The classifier for 5 gestures (fist, pronation, supination, flexion, extension) and idle state was implemented by using a feed-forward neural network with softmax output. Continue reading Towards sensor position-invariant hand gesture recognition using a mechanomyographic interface

https://doi.org/10.1007/978-3-319-54042-9_24

In this paper, a fully functional dataglove device, called CIE-DataGlove, is presented. CIE-DataGlove is a glove-like apparatus intended for human hand posture capture. The essential design requirement was not to hinder hand movement and object manipulation, and not to introduce additional mechanical resistance to the fingers. The system is based on 12 9-DOF inertial measurement units placed on phalanges and metacarpus. The article describes mechanical, hardware, and software implementations used in the development of CIE-DataGlove. Throughput performance of communication interface is studied in the context of interface response time.

https://doi.org/10.1007/978-3-319-54042-9_43

Due to inertial and magnetic sensors imperfections, pre-processing is crucial in obtaining reliable orientation estimates. An easy to implement method of sensor calibration is presented, requiring little to none additional equipment. The method does not need a reference sensor and relies on world magnetic and gravity vectors constant relationship. In addition to standard individual calibration, a cross-sensor reference frame alignment is performed. Comparison with a high-grade MARG unit is also provided. Magnetic inclination stability, inertial and magnetic vector magnitude, and angular error are evaluated.

After two years of absence biolab.put.poznan.pl, webpage about Biomedical Engineering and Biocybernetic Team, is finally back on its feet! Soon we will prepare summaries of all of our research, and developed application projects as well. Stay tuned!