Force classification using surface electromyography from various object lengths and wrist postures

Jitaree, S.; Phukpattaranont, Pornchai

doi:10.1007/s11760-019-01462-z

Cited by 11 publications

(16 citation statements)

References 24 publications

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“…Although still little explored by researchers compared to the classification stage, there is a considerable amount of work published in recent years focusing on the feature selection step. They can be grouped into the search for the best channels set (Batayneh et al 2020;Hua et al 2019;Krasoulis et al 2020;Sun et al 2020;Wang et al 2019;Yu et al 2019), features set (Luo et al 2020;Phukan et al 2019;Yang et al 2020;Ye et al 2019;Zhang et al 2019;Zhou et al 2019) and individual pairs formed by channels and features (Castiblanco et al 2020;Jair et al 2020;Jitaree & Phukpattaranont 2019;Tosin et al 2020b;Tosin et al 2020a;Tosin et al 2017;Wu et al 2019). Table 4 summarizes the most recent strategies for feature selection stage and the average accuracy presented refer to the best scenario reported by the authors in terms of feature selection method and classifier.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Finally, there is still a research niche within the feature selection stage that considers both the channels and features simultaneously, analyzing the relevance of each pair formed by both individually (Castiblanco et al 2020;Jitaree & Phukpattaranont 2019;Tosin et al 2020b;Tosin et al 2020a;Wu et al 2020). Here, the researches by and by Castiblanco et al (2020) stand out.…”

Section: Feature Selectionmentioning

confidence: 99%

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sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Tosin

Machado²,

Balbinot³

2022

jair

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Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants. This review exposes the current scenario of the movement classification system, providing valuable information for new researchers and guiding future works towards myo-controlled devices.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Tosin

Machado²,

Balbinot³

2022

jair

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“…However, relatively few applications of pattern recognition using force classification are found ( Khan et al, 2021 ). A recent study by Jitaree and Phukpattaranont (2019) classified different levels of forces during pinch grasp for EMG signals within healthy subjects. Al-Timemy et al (2015) classified three broadly divided levels of grip forces using EMG signals from the trans -radial amputees.…”

Section: Introductionmentioning

confidence: 99%

Upper-Limb Electromyogram Classification of Reaching-to-Grasping Tasks Based on Convolutional Neural Networks for Control of a Prosthetic Hand

et al. 2021

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In recent years, myoelectric interfaces using surface electromyogram (EMG) signals have been developed for assisting people with physical disabilities. Especially, in the myoelectric interfaces for robotic hands or arms, decoding the user’s upper-limb movement intentions is cardinal to properly control the prosthesis. However, because previous experiments were implemented with only healthy subjects, the possibility of classifying reaching-to-grasping based on the EMG signals from the residual limb without the below-elbow muscles was not investigated yet. Therefore, we aimed to investigate the possibility of classifying reaching-to-grasping tasks using the EMG from the upper arm and upper body without considering wrist muscles for prosthetic users. In our study, seven healthy subjects, one trans-radial amputee, and one wrist amputee were participated and performed 10 repeatable 12 reaching-to-grasping tasks based on the Southampton Hand Assessment Procedure (SHAP) with 12 different weighted (light and heavy) objects. The acquired EMG was processed using the principal component analysis (PCA) and convolutional neural network (CNN) to decode the tasks. The PCA–CNN method showed that the average accuracies of the healthy subjects were 69.4 ± 11.4%, using only the EMG signals by the upper arm and upper body. The result with the PCA–CNN method showed 8% significantly higher accuracies than the result with the widely used time domain and auto-regressive-support vector machine (TDAR–SVM) method as 61.6 ± 13.7%. However, in the cases of the amputees, the PCA–CNN showed slightly lower performance. In addition, in the aspects of assistant daily living, because grip force is also important when grasping an object after reaching, the possibility of classifying the two light and heavy objects in each reaching-to-grasping task was also investigated. Consequently, the PCA–CNN method showed higher accuracy at 70.1 ± 9.8%. Based on our results, the PCA–CNN method can help to improve the performance of classifying reaching-to-grasping tasks without wrist EMG signals. Our findings and decoding method can be implemented to further develop a practical human–machine interface using EMG signals.

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“…Al-Timemy et al investigated the force variation effect, where subjects were asked to perform 4 different grip patterns [19]. It must be noticed that previous studies used the commercially available force sensors and grip dynamometers were more conventional to measure grip force [20][21][22]. Few researchers have used grip dynamometers to measure grip force at the variable grip span capability [20,23].…”

Section: Introductionmentioning

confidence: 99%

EMG based classification for pick and place task

Khan¹,

Khan²,

Farooq³

2021

Biomed. Phys. Eng. Express

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The hand amputee is deprived of number of activities of daily living. To help the hand amputee, it is important to learn the pattern of muscles activity. There are several elements of tasks, which involve forearm along with the wrist and hand. The one very important task is pick and place activity performed by the hand. A pick and place action is a compilation of different finger motions for the grasping of objects at different force levels. This action may be better understood by learning the electromyography signals of forearm muscles. Electromyography is the technique to acquire electrical muscle activity that is used for the pattern recognition technique of assistive devices. Regarding this, the different classification characterizations of EMG signals involved in the pick and place action, subjected to variable grip span and weights were considered in this study. A low-level force measuring gripper, capable to bear the changes in weights and object spans was designed and developed to simulate the task. The grip span varied from 6 cm to 9 cm and the maximum weight used in this study was 750 gms. The pattern recognition classification methodology was performed for the differentiation of phases of the pick and place activity, grip force, and the angular deviation of metacarpal phalangeal (MCP) joint. The classifiers used in this study were decision tree (DT), support vector machines (SVM) and k-nearest neighbor (k-NN) based on the feature sets of the EMG signals. After analyses, it was found that k-NN performed best to classify different phases of the activity and relative deviation of MCP joint with an average classification accuracy of 82% and 91% respectively. However; the SVM performed best in classification of force with a particular feature set. The findings of the study would be helpful in designing the assistive devices for hand amputee.

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Force classification using surface electromyography from various object lengths and wrist postures

Cited by 11 publications

References 24 publications

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Upper-Limb Electromyogram Classification of Reaching-to-Grasping Tasks Based on Convolutional Neural Networks for Control of a Prosthetic Hand

EMG based classification for pick and place task

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