Influence of sEMG electrode matrix configuration on hand gesture recognition performance

Tomczyński, Jakub; Mańkowski, Tomasz; Kaczmarek, Piotr

doi:10.23919/spa.2017.8166835

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…This is because CapgMyo has 72 more votes than NinaPro DB1. In addition, the increasing number of sEMG channels can bring more information, and also improve the recognition accuracy [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Zhao

Wang

et al. 2021

Sensors

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In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

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Section: Discussionmentioning

confidence: 99%

Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Zhao

Wang

et al. 2021

Sensors

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“…For each attending person, the armband is positioned on the right forearm, at one third of the line between the elbow and the wrist, with CH 1 (i.e., the master board) above the Extensor Digitorum. This position was previously determined, both according to literature reference positions [53]- [55] and by consulting with some fellow physicians about how deep and superficial muscles interact in this area. In particular, the forearm muscles have a very small body section along all their limb segment, resulting in a lot of cross actions between adjacent ones.…”

Section: H Acquisition Protocol and Dataset Creationmentioning

confidence: 99%

Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband

Mongardi

Rossi

Prestia

et al. 2022

IEEE Trans. Biomed. Circuits Syst.

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Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g., surface Elec-troMyoGraphy (sEMG) or PhotoPlethysmoGraphy (PPG). In the last few years, the interest demonstrated by both academia and industry brought to a continuous spawning of commercial and custom wearable devices, which tried to address different challenges in many application fields, from tele-rehabilitation to sign language recognition. In this work, we propose a novel 7-channel sEMG armband, which can be employed as HMI for both serious gaming control and rehabilitation support. In particular, we designed the prototype focusing on the capability of our device to compute the Average Threshold Crossing (ATC) parameter, which is evaluated by counting how many times the sEMG signal crosses a threshold during a fixed time duration (i.e., 130 ms), directly on the wearable device. Exploiting the event-driven characteristic of the ATC, our armband is able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices. At the end of an acquisition campaign that involved the participation of 26 people, we obtained an average classifier accuracy of 91.9 % when aiming to recognize in real time 8 active hand gestures plus the idle state. Furthermore, with 2.92 mA of current absorption during active functioning and 1.34 ms prediction latency, this prototype confirmed our expectations and can be an appealing solution for longterm (up to 60 h) medical and consumer applications.

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“…putEMG dataset consists of a series of trials that include execution of 8 hand gestures (see Figure 3). The gesture choice was based on former studies, active poses showing the highest discrimination [9,25,26] and best fitting for convenient sEMG-based HMI design were selected. The set consists of 7 active gestures — fist , flexion , extension , and 4 pinch gestures where the thumb meets one of the remaining fingers ( pinch index , pinch middle , pinch ring , pinch small —Figure 3e–h).…”

Section: Putemg Data Collectionmentioning

confidence: 99%

putEMG—A Surface Electromyography Hand Gesture Recognition Dataset

Kaczmarek

Mańkowski

Tomczyński

2019

Sensors

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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). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An 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. The presented dataset can be used as a benchmark for various classification methods, the evaluation of electrode localisation concepts, or the development of classification methods invariant to user-specific features or electrode displacement.

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Influence of sEMG electrode matrix configuration on hand gesture recognition performance

Cited by 4 publications

References 17 publications

Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband

putEMG—A Surface Electromyography Hand Gesture Recognition Dataset

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