Evaluating the influence of subject-related variables on EMG-based hand gesture classification

Riillo, Francesco; Quitadamo, Lucia Rita; Cavrini, Francesco; Saggio, Giovanni; Pinto, C.; Pastò, N. Cosimo; Sbernini, Laura; Gruppioni, Emanuele

doi:10.1109/memea.2014.6860134

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…We investigated the performances of the two approaches by using 32 combinations of features (feature vectors) and 3 different classifiers: linear discriminant analysis (LDA, linear classifier), support vector machines (SVM, non-linear kernel classifier) and artificial neural networks (ANN, non-linear classifier). EMG signals were acquired by means of a low-density sEMG-based device designed for the recognition of hand gestures [21]. Twenty able-bodied (healthy) subjects were recruited to identify ("within" analysis) and compare ("between" analysis) the optimal PCA and CSP pattern recognition parameters (best feature vector/classifier pair).…”

Section: Introductionmentioning

confidence: 99%

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

Riillo

Quitadamo

Cavrini

et al. 2014

Biomedical Signal Processing and Control

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a b s t r a c tWe propose a methodological study for the optimization of surface EMG (sEMG)-based hand gesture classification, effective to implement a human-computer interaction device for both healthy subjects and transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA) approach was compared to the promising supervised common spatial pattern (CSP) methodology to identify the best classification strategy and the related tuning parameters. A low density array of sEMG sensors was built to record the muscular activity of the forearm and classify five different hand gestures. Twenty healthy subjects were recruited to compute optimized parameters for ("within" analysis) and to compare between ("between" analysis) the two strategies. The system was also tested on a transradial amputee subject, in order to assess the robustness of the optimization in recognizing disabled users' gestures.Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accuracy 88.81 ± 6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of 89.35 ± 6.16%). Statistical analysis on classification results shows no significant differences between the two strategies. Moreover we found out that the optimization computed for healthy subjects was proven to be sufficiently robust to be used on the amputee subject. This motivates further investigation of the proposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based hand gesture recognition and constitute a step toward the definition of an efficient EMG-controlled system for amputees.

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Section: Introductionmentioning

confidence: 99%

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

Riillo

Quitadamo

Cavrini

et al. 2014

Biomedical Signal Processing and Control

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“…Different strategies can be applied for training the classification system, including machine-learning approaches [19], to recognize the intentional gesture, such as the index and thumb grip, the three-digital grip, and the fist. Downstream of the classifier, the resulting signal is to actuate the prosthesis, accordingly, as previously pointed out in other works [20].…”

Section: The Sensorized Prosthesismentioning

confidence: 97%

An Interactive Digital-Twin Model for Virtual Reality Environments to Train in the Use of a Sensorized Upper-Limb Prosthesis

Cellupica,

Cirelli,

Saggio

et al. 2024

Algorithms

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In recent years, the boost in the development of hardware and software resources for building virtual reality environments has fuelled the development of tools to support training in different disciplines. The purpose of this work is to discuss a complete methodology and the supporting algorithms to develop a virtual reality environment to train the use of a sensorized upper-limb prosthesis targeted at amputees. The environment is based on the definition of a digital twin of a virtual prosthesis, able to communicate with the sensors worn by the user and reproduce its dynamic behaviour and the interaction with virtual objects. Several training tasks are developed according to standards, including the Southampton Hand Assessment Procedure, and the usability of the entire system is evaluated, too.

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“…The objects refer to those declared by the user as relevant tools or materials at the beginning of each work step. The process enters an idle mode when no hand-object interaction has been detected for a period of time (typically 2 seconds, which was empirically chosen based on preliminary studies for an action in hand-gesture recognition [49]). Hand-object interaction detected after entering an idle mode indicates the starting of a new work step.…”

Section: ) Hand-object Interactionmentioning

confidence: 99%

ARiana: Augmented Reality Based In-Situ Annotation of Assembly Videos

et al. 2022

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Annotated videos are commonly produced for documenting assembly and maintenance processes in the manufacturing industry. However, according to a semi-structured interview we conducted with industrial experts, the current process of creating annotated assembly videos, in which the annotator annotates the video capturing the expert's demonstration of assembly and maintenance process, is cumbersome and time-consuming. The key challenges include three key problems in annotation: (1) unnecessary extra communications between field workers and annotators, (2) lack of suitable camera gear, and (3) wasting time in the manual removal of non-informative portions of captured videos. Because annotation always follows video capture, the problem 1 remains out of scope for state-of-the-art video annotation tools. And making the assumption of a perfect captured video, which covers no occlusion and contains only relevant assembly or maintenance information, causes problem 2 and 3. As a result, we have developed ARiana, a wearable augmented reality-based in-situ video annotation tool that guides field experts to create annotations efficiently while conducting the assembly or maintenance tasks. ARiana has three key features that include context-awareness enabled by hand-object interaction, multimodal interaction for annotation on the fly, and real-time audiovisual guidance enabled by edge offloading. We have implemented ARiana on Android-based smart glasses, equipped with first-person camera and microphone. In a usability test based on attempting to assemble a toy model and to annotate the recorded video simultaneously, ARiana demonstrated higher efficiency and effectiveness compared to one of the state-of-the-art video annotation tools, in which the assembling process is followed by the annotation process. In particular, ARiana helps users finish annotation tasks four times faster, and increase the annotation accuracy by 23%.

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Evaluating the influence of subject-related variables on EMG-based hand gesture classification

Cited by 10 publications

References 14 publications

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

An Interactive Digital-Twin Model for Virtual Reality Environments to Train in the Use of a Sensorized Upper-Limb Prosthesis

ARiana: Augmented Reality Based In-Situ Annotation of Assembly Videos

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