A New EMG Decomposition Framework for Upper Limb Prosthetic Systems

Wu, Wenhao; Jiang, Li; Yang, Bangchu; Gong, Kening; Peng, Chunhao; He, Tianbao

doi:10.1007/s42235-023-00407-0

Cited by 5 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the current BSS methods, CKC and Fast-ICA are the two main methods to identify sources. The new framework adopts the same strategy as CKC to obtain the initial sources, and the peel-off approach is shown to increase the number of MUs correctly identified by CKC under the condition of few channels in the previous study [42]. Therefore, in this study, an algorithm based on ICA proposed by Negro et al [6] was used as a control group.…”

Section: Simulated Signal Decompositionmentioning

confidence: 98%

Decomposition strategy for surface EMG with few channels: a simulation study

Wu,

Jiang,

Yang

2024

J. Neural Eng.

View full text Add to dashboard Cite

Objective. In the specific use of electromyogram (EMG) driven prosthetics, the user's disability reduces the space available for the electrode array. We propose a framework for EMG decomposition adapted to the condition of a few channels (less than 30 observations), which can elevate the potential of prosthetics in terms of cost and applicability. Approach. The new framework contains a peel-off approach, a refining strategy for motor unit spike train (MUST) and motor unit action potential (MUAP) and a re-subtracting strategy to adapt the framework to few channels environments. Simulated EMG signals were generated to test the framework. In addition, we quantify and analyze the effect of strategies used in the framework. Main results. The results show that the new algorithm has an average improvement of 19.97% in the number of motor units (MUs) identified compared to the control algorithm. Quantitative analysis of the usage strategies shows that the re-subtracting and refining strategies can effectively improve the performance of the framework under the condition of few channels. Significance. These prove that the new framework can be applied to few channel conditions, providing a optimization space for neural interface design in cost and user adaptation.

show abstract

Section: Simulated Signal Decompositionmentioning

confidence: 98%

Decomposition strategy for surface EMG with few channels: a simulation study

Wu,

Jiang,

Yang

2024

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

“…where N infer is the total number of inferences, i.e., the validation set size, and the rest follows the notation of Section II. We measure the error as a fraction of the MVC, as is the standard approach [12], [27]- [29]. To rescale forces to the MVC scale, we determined the MVC for each direction (i.e., flexion or extension) of each finger of each subject using the data from HYSER MVC following the same heuristic as suggested by the dataset authors, namely determining the MVC as the average of the 200 strongest values.…”

Section: Regressionmentioning

confidence: 99%

Event-based Estimation of Hand Forces from High-Density Surface EMG on a Parallel Ultra-Low-Power Microcontroller

Zanghieri,

Rapa,

Orlandi

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

parallelism shortens latency Real-time embedded force estimation • accurate & all-fingers • robust cross-day High-Density sEMG Spike trains parallel ultra-low power MCU LIF Neurons 𝐝𝒙 𝐝𝒕 = 𝒙𝐝𝐫𝐢𝐯𝐞 − 𝒙 𝝉 deployment of selected LIFs' dynamics and fitted inference heuristic feature selection reduces computation workload fit of linear readout Abstract-Modeling hand kinematics and dynamics is a key goal for research on Human-Machine Interfaces, with surface electromyography (sEMG) being the most commonly used sensing modality. Though underresearched, sEMG regression-based modeling of hand movements and forces is promising for finer control than allowed by mapping to fixed gestures. We present an event-based sEMG encoding for multi-finger force estimation implemented on a microcontroller unit (MCU). We are the first to target the HYSER High-Density (HD)-sEMG dataset in multi-day conditions closest to a real scenario without a fixed force pattern. Our Mean Absolute Error of (8.4 ± 2.8)% of the Maximum Voluntary Contraction (MVC) is on par with State-of-the-Art (SoA) works on easier settings such as within-day, single-finger, or fixed-exercise. We deploy our solution for HYSER's hardest task on a parallel ultra-low power MCU, getting an energy consumption below 6.5 uJ per sample, 2.8× to 11× more energy-efficient than SoA single-core solutions, and a latency below 280 us per sample, shorter than HYSER's HD-sEMG sampling period, thus compatible with real-time operation on embedded devices.

show abstract

“…Considering the research pertaining to the use of EMG signals, these signals are measured from the surface of the skin [1,2]. The EMG signal-based techniques for grasp classification are discussed in [3][4][5][6]. The EMG signal-based prosthetic hands are often found difficult to control, owing to the user skills required to generate the required level of the signal, as well as the speed to make decisions in order to activate the appropriate muscles.…”

Section: Introductionmentioning

confidence: 99%

Vision-aided grasp classification: design and evaluation of compact CNN for prosthetic hands

Sharma,

Vasamsetti,

Chander

et al. 2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Powered prosthetic hands capable of executing various grasp patterns are highly sought-after solutions for upper limb amputees. A crucial requirement for such prosthetic hands is the accurate identification of the intended grasp pattern and subsequent activation of the prosthetic digits accordingly. Vision-based grasp classification techniques offer improved coordination between amputees and prosthetic hands without physical contact. Deep learning methods, particularly Convolutional Neural Networks (CNNs), are utilized to process visual information for classification. The key challenge lies in developing a model that can effectively generalize across various object shapes and accurately classify grasp classes. To address this, a compact CNN model named GraspCNet is proposed, specifically designed for grasp classification in prosthetic hands. The use of separable convolutions reduces the computational burden, making it potentially suitable for real-time applications on embedded systems. The GraspCNet model is designed to learn and generalize from object shapes, allowing it to effectively classify unseen objects beyond those included in the training dataset. The proposed model was trained and tested using various standard object data sets. A cross-validation strategy has been adopted to perform better in seen and unseen object class scenarios. The average accuracy achieved was 82.22% and 75.48% in the case of seen, and unseen object classes respectively. In computer-based real-time experiments, the GraspCNet model achieved an accuracy of 69%. A comparative analysis with state-of-the-art techniques revealed that the proposed GraspCNet model outperformed most benchmark techniques and demonstrated comparable performance with the DcnnGrasp method. The compact nature of the GraspCNet model suggests its potential for integration with other sensing modalities in prosthetic hands.

show abstract

A New EMG Decomposition Framework for Upper Limb Prosthetic Systems

Cited by 5 publications

References 36 publications

Decomposition strategy for surface EMG with few channels: a simulation study

Decomposition strategy for surface EMG with few channels: a simulation study

Event-based Estimation of Hand Forces from High-Density Surface EMG on a Parallel Ultra-Low-Power Microcontroller

Vision-aided grasp classification: design and evaluation of compact CNN for prosthetic hands

Contact Info

Product

Resources

About