Publications

@article{Tsinganos2022,

title = {Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks},

author = {Panagiotis Tsinganos and Bart Jansen and Jan Cornelis and Athanassios Skodras},

editor = {Antonio Fernández-Caballero and Juan M. Corchado},

url = {https://www.mdpi.com/1424-8220/22/5/1694},

doi = {10.3390/s22051694},

year  = {2022},

date = {2022-02-22},

urldate = {2022-02-22},

journal = {Sensors},

volume = {22},

number = {5},

pages = {1694},

abstract = {In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. Recently, a specific class of these models called Temporal Convolutional Networks (TCNs) has been successfully applied to this task. (2) Methods: In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using TCNs. Specifically, we investigate the real-time behavior of our previous TCN model by performing a simulation experiment on a recorded sEMG dataset. (3) Results: The proposed network trained with data augmentation yields a small improvement in accuracy compared to our existing model. However, the classification accuracy is decreased in the real-time evaluation, showing that the proposed TCN architecture is not suitable for such applications. (4) Conclusions: The real-time analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.},

keywords = {attention, CNN, Deep learning, hand gesture recognition, real time, semg, TCN},

pubstate = {published},

tppubtype = {article}

}

@article{Tsinganos2021,

title = {The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals},

author = {Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras},

url = {https://ijeces.ferit.hr/index.php/ijeces/article/view/345},

doi = {10.32985/ijeces.12.1.3},

issn = {1847-6996},

year  = {2021},

date = {2021-04-21},

journal = {International Journal of Electrical and Computer Engineering Systems},

volume = {12},

number = {1},

pages = {23 - 31},

abstract = {Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms  changed,  but  also  the  performance  in  various  classification  and  prediction  tasks  has  been  significantly  improved  with  respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of  image  representations  from  multi-electrode  sEMG  signals,  by  investigating  how  the  Hilbert  curve  compares  to  the  Peano-  and  Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures.},

keywords = {classification, CNN, Deep learning, electromyography, hand gesture recognition, hilbert curve, Peano curve, semg, space-filling curve, Z-order curve},

pubstate = {published},

tppubtype = {article}

}

@article{Tsinganos2020b,

title = {Data Augmentation of Surface Electromyography for Hand Gesture Recognition},

author = {Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras},

doi = {10.3390/s20174892},

issn = {1424-8220},

year  = {2020},

date = {2020-08-29},

journal = {Sensors},

volume = {20},

number = {17},

pages = {4892},

abstract = {The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.},

keywords = {CNN, data augmentation, Deep learning, electromyography, hand gesture recognition, semg},

pubstate = {published},

tppubtype = {article}

}

@article{Tsinganos2020,

title = {Hilbert sEMG data scanning for hand gesture recognition based on deep learning},

author = {Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras},

url = {https://github.com/DSIP-UPatras/sEMG-hilbert-curve},

doi = {10.1007/s00521-020-05128-7},

isbn = {1433-3058},

year  = {2020},

date = {2020-07-07},

journal = {Neural Computing and Applications},

abstract = {Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed toward surface electromyography (sEMG)-based gesture recognition, often addressed as an image classification problem using convolutional neural networks (CNNs). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals, which allows the application of typical image processing pipelines such as CNNs on sequence data. The proposed method is evaluated on different state-of-the-art network architectures and yields a significant classification improvement over the approach without the Hilbert curve. Additionally, we develop a new network architecture (MSHilbNet) that takes advantage of multiple scales of an initial Hilbert curve representation and achieves equal performance with fewer convolutional layers.},

keywords = {classification, CNN, Deep learning, electromyography, hand gesture recognition, hilbert curve, Multi-scale, semg},

pubstate = {published},

tppubtype = {article}

}