Portable EMG Data Acquisition Module for Upper Limb Prosthesis Application

Pancholi, Sidharth; Joshi, Amit M.

doi:10.1109/jsen.2018.2809458

Cited by 79 publications

(41 citation statements)

References 36 publications

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“…The sensor interfaces are mostly based on commercial SoA Analog Front Ends, specifically designed for bio-potential acquisition. Regarding computational platforms and algorithms, top performance classification methods rely on supervised machine learning algorithms to reliably classify acquired data, such as SVM, LDA [24] or ANN [7]. The recognition accuracy of these algorithms is above 85%, and they are implementable on wearable platforms [19].…”

Section: Related Workmentioning

confidence: 99%

“…The recognition accuracy of these algorithms is above 85%, and they are implementable on wearable platforms [19]. Despite some other methods, like RLS [9], can solve multiclass problems with negligible computational overhead, deterministic training time, and performance comparable to the aforementioned algorithms, in this work we performed a quantitative comparison with SVM, which represents the SoA algorithm and widely accepted baseline framework for EMG-based pattern recognition, already tested in several embedded implementations [24]- [29].…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, their computational and memory requirements severely hamper their implementation on resource-constrained platforms. For instance, LDA, which has a training faster than SVM and ANN [16], has time complexity of O(mnt + t 3 ) with a memory footprint of O(mn + mt + nt) memory, where m is the number of samples, n is the number of features and t is defined as min(m, n) [24]. As a result, whereas the number of samples/features grows, the system becomes too resource-hungry to be adapted to a low-power platform.…”

Section: Related Workmentioning

confidence: 99%

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Online Learning and Classification of EMG-Based Gestures on a Parallel Ultra-Low Power Platform Using Hyperdimensional Computing

Benatti

Montagna

Kartsch

et al. 2019

IEEE Trans. Biomed. Circuits Syst.

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This work presents a wearable EMG gesture recognition system based on the hyperdimensional (HD) computing paradigm, running on a programmable Parallel Ultra-Low-Power (PULP) platform. The processing chain includes efficient on-chip training, which leads to a fully embedded implementation with no need to perform any offline training on a personal computer. The proposed solution has been tested on 10 subjects in a typical gesture recognition scenario achieving 85% average accuracy on 11 gestures recognition, which is aligned with the State-Of-the-Art (SoA), with the unique capability of performing online learning. Furthermore, by virtue of the Hardware (HW) friendly algorithm and of the efficient PULP System-on-Chip (SoC) (Mr. Wolf) used for prototyping and evaluation, the energy budget required to run the learning part with 11 gestures is 10.04mJ, and 83.2µJ per classification. The system works with a average power consumption of 10.4mW in classification, ensuring around 29h of autonomy with a 100mAh battery. Finally, the scalability of the system is explored by increasing the number of channels (up-to 256 electrodes), demonstrating the suitability of our approach as universal, energy-efficient biopotential wearable recognition framework.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Online Learning and Classification of EMG-Based Gestures on a Parallel Ultra-Low Power Platform Using Hyperdimensional Computing

Benatti

Montagna

Kartsch

et al. 2019

IEEE Trans. Biomed. Circuits Syst.

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“…The commonly used feature can be mainly divided into time domain feature, frequency domain feature, and timefrequency domain feature. For the time domain feature, mean absolute value (MAV) [27][28][29][30][31][32], root mean square (RMS) [29,31], variance (VAR) [29,31], standard deviation (SD) [29], zero count (ZC) [27,29,32], waveform length (WL) [27,29,32], slope sign change (SSC) [29,32], integrated EMG (IEMG) [33], and difference of mean absolute value (DMAV) [27] are commonly utilized. Although the calculation of time domain feature is simple, it is not enough to describe the information of signals.…”

Section: Machine Learning Based Discrete-motion Classificationmentioning

confidence: 99%

sEMG Based Human Motion Intention Recognition

Zhang

Liu

Han

et al. 2019

Journal of Robotics

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Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

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“…In [6] they create a 5-channel system based on operational amplifiers, a signal acquisition system is proposed, from which nine characteristics have been extracted in the time domain and 7 in the frequency domain; which are classified with different algorithms achieving a precision that goes from 57.69% to 99.92%.…”

Section: Introductionmentioning

confidence: 99%

Electromyography Signal Acquisition and Analysis System for Finger Movement Classification

Alvarado-Diaz¹,

Meneses-Claudio²,

Román-Gonzalez³

2019

IJACSA

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Electromyography (EMG) is very important to capture muscle activity. Although many jobs establish data acquisition system, however, it is also essential to demonstrate that these data are reliable. In this sense, one proposes a design and implementation of a data acquisition system with the Myoware device and the ATmega329P microcontroller. One also proved its reliability by classifying the movement of the fingers of the hand, with the help of the algorithm k-Nearest Neighbors (KNN) and the application of Classification Learner code of Matlab. The results show a success rate of 99.1%.

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Portable EMG Data Acquisition Module for Upper Limb Prosthesis Application

Cited by 79 publications

References 36 publications

Online Learning and Classification of EMG-Based Gestures on a Parallel Ultra-Low Power Platform Using Hyperdimensional Computing

Online Learning and Classification of EMG-Based Gestures on a Parallel Ultra-Low Power Platform Using Hyperdimensional Computing

sEMG Based Human Motion Intention Recognition

Electromyography Signal Acquisition and Analysis System for Finger Movement Classification

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