Deep neural network for EMG signal classification of wrist position: Preliminary results

Orjuela-Cañón, Álvaro David; Ruiz-Olaya, Andres F.; Forero, Leonardo

doi:10.1109/la-cci.2017.8285706

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…In this context, deep learning frameworks are getting more attention because they can handle and classify raw signals without the need of previous data processing steps. As for the feature extraction methods, most of the techniques adopted for ENG signal classification have been previously employed in the analysis of EMG activity, like CNN [23], ANN [24], Spiking Neural Network (SNN) [25] and Recurrent Neural Network (RNN) [26]. In a recent work, Porta et al [27] obtained almost 84% mean accuracy in discriminating ten different stimuli (touch, nociception, dorsiflexion and plantarflexion at different intensities) with both CNN and SNN.…”

Section: Related Workmentioning

confidence: 99%

Classification of Sensory Neural Signals through Deep Learning Methods

Vasta,

Coviello,

Spagnolini

et al. 2023

IEEE EUROCON 2023 - 20th International Conference on Smart Technologies

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The recording and analysis of peripheral neural signals can be beneficial to provide feedback to prosthetic limbs and recover the sensory functionality in people with nerve injuries. Nevertheless, the interpretation of sensory recordings extracted from the nerve is not trivial, and only few studies have applied classifiers on sequences of neural signals without previous feature extraction. This paper evaluates the classification performance of two deep learning (DL) models (CNN and ConvLSTM) applied to the electroneurographic (ENG) activity recorded from the sciatic nerve of rats. The ENG signals, available from two public datasets, were recorded using multi-channel cuff electrodes in response to four sensory inputs (plantarflexion, dorsiflexion, nociception, and touch) elicited in response to mechanical stimulation applied to the hind paw of the rats. Different temporal lengths of the signals were considered (2.5 s, 1 s, 500 ms, 200 ms, and 100 ms). Both the two DL models proved to correctly discriminate sensory stimuli without the need of hand-engineering feature extraction. Moreover, ConvLSTM outperformed state-of-the-art results in classifying sensory ENG activity (more than 90% F1-score for sequences greater than 500 ms), and it showed promising results for real-time application scenarios.

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Section: Related Workmentioning

confidence: 99%

Classification of Sensory Neural Signals through Deep Learning Methods

Vasta,

Coviello,

Spagnolini

et al. 2023

IEEE EUROCON 2023 - 20th International Conference on Smart Technologies

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“…A deep neural network (DNN) has been chosen for dealing with real-world signal processing tasks, due to its outstanding performance compared to other machine learning algorithms (Park and Lee, 2016;Chen et al, 2017;Orjuela-Cañón et al, 2017;Tsinganos et al, 2018;Chaiyaroj et al, 2019). Motivated by this fact and considering our aim for a real-time system, we implemented a simple feed-forward neural network model.…”

Section: Deep Neural Network Classifiermentioning

confidence: 99%

Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

Sri-iesaranusorn

Chaiyaroj

Buekban

et al. 2021

Front. Bioeng. Biotechnol.

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Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the user to actively control the prosthesis. However, results reported by previous studies on using sEMG for hand and wrist movement classification vary by a large margin, due to several factors including but not limited to the number of classes and the acquisition protocol. The objective of this paper is to investigate the deep neural network approach on the classification of 41 hand and wrist movements based on the sEMG signal. The proposed models were trained and evaluated using the publicly available database from the Ninapro project, one of the largest public sEMG databases for advanced hand myoelectric prosthetics. Two datasets, DB5 with a low-cost 16 channels and 200 Hz sampling rate setup and DB7 with 12 channels and 2 kHz sampling rate setup, were used for this study. Our approach achieved an overall accuracy of 93.87 ± 1.49 and 91.69 ± 4.68% with a balanced accuracy of 84.00 ± 3.40 and 84.66 ± 4.78% for DB5 and DB7, respectively. We also observed a performance gain when considering only a subset of the movements, namely the six main hand movements based on six prehensile patterns from the Southampton Hand Assessment Procedure (SHAP), a clinically validated hand functional assessment protocol. Classification on only the SHAP movements in DB5 attained an overall accuracy of 98.82 ± 0.58% with a balanced accuracy of 94.48 ± 2.55%. With the same set of movements, our model also achieved an overall accuracy of 99.00% with a balanced accuracy of 91.27% on data from one of the amputee participants in DB7. These results suggest that with more data on the amputee subjects, our proposal could be a promising approach for controlling versatile prosthetic hands with a wide range of predefined hand and wrist movements.

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“…In recent years, the development of sEMG detection systems has been propelled by advances in sensor technology, signal processing, and machine learning. These systems typically involve the use of electromyography electrodes to measure muscle activity in various body parts such as the arm [ 1 , 2 ], leg [ 3 , 4 ], and face [ 5 ], which are then amplified, filtered, and digitized for analysis. However, the performance of these systems is often limited by factors such as noise and the complexity of the signal itself [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Wireless, High-Quality, Soft and Portable Wrist-Worn System for sEMG Signal Detection

et al. 2023

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The study of wearable systems based on surface electromyography (sEMG) signals has attracted widespread attention and plays an important role in human–computer interaction, physiological state monitoring, and other fields. Traditional sEMG signal acquisition systems are primarily targeted at body parts that are not in line with daily wearing habits, such as the arms, legs, and face. In addition, some systems rely on wired connections, which impacts their flexibility and user-friendliness. This paper presents a novel wrist-worn system with four sEMG acquisition channels and a high common-mode rejection ratio (CMRR) greater than 120 dB. The circuit has an overall gain of 2492 V/V and a bandwidth of 15~500 Hz. It is fabricated using flexible circuit technologies and is encapsulated in a soft skin-friendly silicone gel. The system acquires sEMG signals at a sampling rate of over 2000 Hz with a 16-bit resolution and transmits data to a smart device via low-power Bluetooth. Muscle fatigue detection and four-class gesture recognition experiments (accuracy greater than 95%) were conducted to validate its practicality. The system has potential applications in natural and intuitive human–computer interaction and physiological state monitoring.

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Deep neural network for EMG signal classification of wrist position: Preliminary results

Cited by 13 publications

References 21 publications

Classification of Sensory Neural Signals through Deep Learning Methods

Classification of Sensory Neural Signals through Deep Learning Methods

Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

A Wireless, High-Quality, Soft and Portable Wrist-Worn System for sEMG Signal Detection

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