Myoelectric digit action decoding with multi-label, multi-class classification: an offline analysis

Krasoulis, Agamemnon; Nazarpour, Kianoush

doi:10.1101/2020.03.24.005710

Cited by 3 publications

(5 citation statements)

References 47 publications

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“…We extracted two EMG features from each channel, namely, waveform length and log-variance [5]. We based our selection on previous findings showing that these features are effective both for multi-output regression and multilabel classification [14,23]. We used the same EMG processing pipeline for the two control schemes presented in the following section.…”

Section: Signal Pre-processingmentioning

confidence: 99%

“…We set the action step for the "open" and "close" commands such that a single-DOF movement from the bottom to top position, or vice-versa, would require 1.5 s. This translated in using an action step of 0.043. Based on previous findings [23], we trained six independent linear discriminant analysis (LDA) classifiers, one for each available DOF.…”

Section: Action Controlmentioning

confidence: 99%

“…position) control in a robotic hand tele-operation task with an instrumented data glove [22]. In addition, we have performed a systematic offline investigation of multilabel classification for digit action decoding [23]. Here, we provide for the first time a real-time implementation of the algorithm for myoelectric digit control.…”

Section: Introductionmentioning

confidence: 99%

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Discrete action control for prosthetic digits

Krasoulis

Nazarpour

2020

Preprint

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Objective We aim to develop a paradigm for simultaneous and independent control of multiple degrees of freedom (DOFs) for upper-limb prostheses. Approach We introduce action control, a novel method to operate prosthetic digits with surface electromyography (EMG) based on multi-label, multi-class classification. At each time step, the decoder classifies movement intent for each controllable DOF into one of three categories: open, close, or stall (i.e., no movement). We implemented a real-time myoelectric control system using this method and evaluated it by running experiments with one unilateral and two bilateral amputees. Participants controlled a six-DOF bar interface on a computer display, with each DOF corresponding to a motor function available in multi-articulated prostheses. Main results We show that action control can significantly and systematically outperform the state-of-the-art method of position control via multi-output regression in both task-and non-task-related measures. Improvements in median task performance ranged from 20.14% to 62.32% for individual participants. Analysis of a post-experimental survey revealed that all participants rated action higher than position control in a series of qualitative questions and expressed an overall preference for the former. Significance Action control has the potential to improve the dexterity of upper-limb prostheses. In comparison with regression-based systems, it only requires discrete instead of real-valued ground truth labels, typically collected with motion tracking systems. This feature makes the system both practical in a clinical setting and also suitable for bilateral amputation. This work is the first demonstration of myoelectric digit control in bilateral upper-limb amputees. Further investigation and pre-clinical evaluation are required to assess the translational potential of the method.Discrete action control for prosthetic digits

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Section: Signal Pre-processingmentioning

confidence: 99%

Section: Action Controlmentioning

confidence: 99%

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Discrete action control for prosthetic digits

Krasoulis

Nazarpour

2020

Preprint

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“…Alternative approaches to hand kinematics focus on the velocity of hand joint movements. For instance, velocity was targeted in [13], adopting a hybrid classification-regression setup which thresholds speed into 3 levels, thus still limiting the prediction to discrete classes. A completely orthogonal approach for sEMG regression focuses on hand dynamics instead of kinematics.…”

Section: Introduction and Related Workmentioning

confidence: 99%

sEMG-based Regression of Hand Kinematics with Temporal Convolutional Networks on a Low-Power Edge Microcontroller

Zanghieri

Benatti

Burrello

et al. 2021

2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS)

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Human-Machine Interfaces based on gesture control are a very active field of research, aiming to enable natural interaction with objects. A successful State-of-the-Art (SoA) methodology for robotic hand control relies on the surface electromyographic (sEMG) signal, a non-invasive approach that can provide accurate and intuitive control when coupled with decoding algorithms based on Deep Learning (DL). However, the vast majority of the approaches so far have focused on sEMG classification, producing control systems that limit gestures to a predefined set of positions. In contrast, sEMG regression is still a new field, providing a more natural and complete control method that returns the complete hand kinematics. This work proposes a regression framework based on TEMPONet, a SoA Temporal Convolutional Network (TCN) for sEMG decoding, which we further optimize for deployment. We test our approach on the NinaPro DB8 dataset, targeting the estimation of 5 continuous degrees of freedom for 12 subjects (10 able-bodied and 2 trans-radial amputees) performing a set of 9 contralateral movements. Our model achieves a Mean Absolute Error of 6.89°, which is 0.15°better than the SoA. Our TCN reaches this accuracy with a memory footprint of only 70.9 kB, thanks to int8 quantization. This is remarkable since high-accuracy SoA neural networks for sEMG can reach sizes up to tens of MB, if deployment-oriented reductions like quantization or pruning are not applied. We deploy our model on the GAP8 edge microcontroller, obtaining 4.76 ms execution latency and an energy cost per inference of 0.243 mJ, showing that our solution is suitable for implementation on resourceconstrained devices for real-time control.Clinical relevance -The proposed setup enables the deployment of sEMG-based regression of hand kinematics for mechanical hand control via embedded devices, granting naturalness and accuracy with extremely low delay and energy consumption.

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“…The HGRs include three movements (elbow extension, shoulder extension, combined shoulder and elbow extension), and a case with no movement (default condition). Simultaneous and independent control of multi degrees of freedom (DoF), such as elbow and shoulder joints, is the main target of the machine learning-based model for controlling robotic arm using electromyography (EMG) signal [44]. This research also focused on the positioning of the EMG sensor on the target muscles that are directly involved in the movement of the upper arm.…”

Section: Introductionmentioning

confidence: 99%

Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning

et al. 2021

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This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification.

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Myoelectric digit action decoding with multi-label, multi-class classification: an offline analysis

Cited by 3 publications

References 47 publications

Discrete action control for prosthetic digits

Discrete action control for prosthetic digits

sEMG-based Regression of Hand Kinematics with Temporal Convolutional Networks on a Low-Power Edge Microcontroller

Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning

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