scholarly journals A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 578
Author(s):  
Yumiao Chen ◽  
Zhongliang Yang ◽  
Yangliang Wen
Keyword(s):  
Hybrid Model ◽  
Low Cost ◽  
Surface Emg ◽  
Geometrical Parameters ◽  
Motion Patterns ◽  
Hand Motion ◽  
Model Combining ◽  
Bilateral Training ◽  
Soft Actuators ◽  
Semg Signals

Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about −2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance.

A Multichannel Surface EMG System for Hand Motion Recognition

2015 ◽  
Vol 12 (02) ◽  
pp. 1550011 ◽  
Author(s):  
Yinfeng Fang ◽  
Honghai Liu ◽  
Gongfa Li ◽  
Xiangyang Zhu
Keyword(s):  
Root Mean Square ◽  
Surface Emg ◽  
Signal Acquisition ◽  
Mean Square ◽  
Semg Signal ◽  
Motion Recognition ◽  
Hand Motion ◽  
Prosthetic Hands ◽  
The Relationship ◽  
Semg Signals

Surface electromyography (sEMG)-based hand motion recognition has a variety of promising applications. While a person performs different hand motions, commands can be extracted to control external devices, such as prosthetic hands, tablets and so forth. The acquisition of discriminative sEMG signals determines the accuracy of intended control commands extraction. This paper develops an 16-channel sEMG signal acquisition system with a novel electrode configuration that is specially designed to collect sEMG on the forearm. Besides, to establish the relationship between multichannel sEMG signals and hand motions, a 2D EMG map is designed. Inspired from the electromyographic (EMG) map, this paper proposes an EMG feature named differential root mean square (DRMS) that somewhat takes the relationship between neighboring EMG channels into account. In the task of four hand motion discrimination by K-means and fuzzy C-means, DRMS outperforms traditional root mean square (RMS) by 29.0% and 36.8%, respectively. The findings of this paper support and guide the use of sEMG techniques to investigate sEMG-based hand motion recognition.

scholarly journals Low-cost Active Dry-Contact Surface EMG Sensor for Bionic Arms

2020 ◽  
Author(s):  
Asma M. Naim ◽  
Kithmin Wickramasinghe ◽  
Ashwin De Silva ◽  
Malsha V. Perera ◽  
Thilina Dulantha Lalitharatne ◽  
...  
Keyword(s):  
Contact Surface ◽  
Low Cost ◽  
Surface Emg ◽  
Dry Contact

A new hybrid model combining EMD and neural network for multi-step ahead load forecasting

2021 ◽  
pp. 1-16
Author(s):  
Hasmat Malik ◽  
Majed A. Alotaibi ◽  
Abdulaziz Almutairi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Hybrid Model ◽  
Storage System ◽  
Load Forecasting ◽  
Model Performance ◽  
Energy Storage System ◽  
Electric Load ◽  
Real Time Analysis ◽  
Model Combining

The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error).

scholarly journals sEMG Signal Acquisition Strategy towards Hand FES Control

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Cinthya Lourdes Toledo-Peral ◽  
Josefina Gutiérrez-Martínez ◽  
Jorge Airy Mercado-Gutiérrez ◽  
Ana Isabel Martín-Vignon-Whaley ◽  
Arturo Vera-Hernández ◽  
...  
Keyword(s):  
Electrode Placement ◽  
Signal Acquisition ◽  
Semg Signal ◽  
Motion Patterns ◽  
Upper Limb Rehabilitation ◽  
Hand Motion ◽  
Hand Dexterity ◽  
Severe Fatigue ◽  
Patients Experience ◽  
Limb Rehabilitation

Due to damage of the nervous system, patients experience impediments in their daily life: severe fatigue, tremor or impaired hand dexterity, hemiparesis, or hemiplegia. Surface electromyography (sEMG) signal analysis is used to identify motion; however, standardization of electrode placement and classification of sEMG patterns are major challenges. This paper describes a technique used to acquire sEMG signals for five hand motion patterns from six able-bodied subjects using an array of recording and stimulation electrodes placed on the forearm and its effects over functional electrical stimulation (FES) and volitional sEMG combinations, in order to eventually control a sEMG-driven FES neuroprosthesis for upper limb rehabilitation. A two-part protocol was performed. First, personalized templates to place eight sEMG bipolar channels were designed; with these data, a universal template, called forearm electrode set (FELT), was built. Second, volitional and evoked movements were recorded during FES application. 95% classification accuracy was achieved using two sessions per movement. With the FELT, it was possible to perform FES and sEMG recordings simultaneously. Also, it was possible to extract the volitional and evoked sEMG from the raw signal, which is highly important for closed-loop FES control.

Surface EMG and Upper-Limb Rehabilitation

2011 ◽  
pp. 335-343
Author(s):  
Kazuya Funada ◽  
Jinglong Wu ◽  
Satoshi Takahashi
Keyword(s):  
Experimental System ◽  
Surface Emg ◽  
Passive Movement ◽  
Measuring Device ◽  
Upper Limb Rehabilitation ◽  
Hand Motion ◽  
Characteristic Points ◽  
Opening And Closing ◽  
Limb Rehabilitation ◽  
Simple Measuring

In rehabilitating hemiplegic patients, purposeful movements such as the opening and closing of hands are reported to be more effective than passive movement with an instrument. The authors of this chapter used surface electromyogram (surface EMG) signals as a way to convey the patient’s conscious ability to open and close their hands. The muscles in the forearm contract when the hand is closed or opened, which creates a simple signal that is comparatively easy to measure with surface EMG, a simple measuring device. The action potentials of the muscles involved in the opening-and-closing motions of hands were measured from several points in the forearm when those muscles contracted, and their distribution was analyzed. The purpose of this study is to develop a simple system to recognize the movement of a patient’s hand using measurements of EMG signals from only the most characteristic points on the forearm to replace similar, but more complex, research such as multi-channel measurement and wave analysis by FFT. The authors specified the optimum measuring points on the palm and dorsal sides of the forearm for the recognition of hand motion by the experimental system. This system successfully recognized hand motion through the analysis of the surface EMG signals measured from only two optimum points to allow arbitrary control of the rehabilitation device based on the recognition results.

Development of a Surface EMG-Based Control System for Controlling Assistive Devices

2020 ◽  
pp. 765-785
Author(s):  
Uvanesh K. ◽  
Suraj Kumar Nayak ◽  
Biswajeet Champaty ◽  
Goutam Thakur ◽  
Biswajit Mohapatra ◽  
...  
Keyword(s):  
Control System ◽  
Wavelet Transforms ◽  
High Efficiency ◽  
Surface Emg ◽  
Assistive Device ◽  
Discrete Wavelet ◽  
Motor Disability ◽  
Robotic Vehicle ◽  
The Time Domain ◽  
Semg Signals

The current study discusses about the development of an EMG based wireless control system for the patients suffering from high-level motor disability. Surface EMG (sEMG) signals were processed in the time domain and using discrete wavelet transforms (DWT). The statistical features of the signals (sEMG, envelope of the squared sEMG and wavelet processed sEMG) were determined and analyzed. The analysis of the features suggested that the features of the envelope of the squared sEMG signals were sufficient to be used for high-efficiency classification and control signal generation. A hall-effect sensor based switching mechanism was introduced for controlling the duration of the activation of the device. The control signals were wirelessly transmitted to the assistive device (robotic vehicle). The training and the subsequent use of the developed control system were easy.

scholarly journals Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3144 ◽  
Author(s):  
Sherif Said ◽  
Ilyes Boulkaibet ◽  
Murtaza Sheikh ◽  
Abdullah S. Karar ◽  
Samer Alkork ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Experimental Test ◽  
Support Vector Machine Classifier ◽  
Low Cost ◽  
Support Vector ◽  
Wide Range ◽  
3D Printed ◽  
Semg Signals

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

scholarly journals Combining Independent Component and Grey Relational Analysis for the Real-Time System of Hand Motion Identification Using Bend Sensors and Multichannel Surface EMG

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Pei-Jarn Chen ◽  
Yi-Chun Du
Keyword(s):  
Grey Relational Analysis ◽  
Processing Time ◽  
Back Propagation ◽  
Surface Emg ◽  
Independent Component ◽  
Real Time System ◽  
Data Glove ◽  
Hand Motion ◽  
Relational Analysis ◽  
Grey Relational

This paper proposes a portable system for hand motion identification (HMI) using the features from data glove with bend sensors and multichannel surface electromyography (SEMG). SEMG could provide the information of muscle activities indirectly for HMI. However it is difficult to discriminate the finger motion like extension of thumb and little finger just using SEMG; the data glove with five bend sensors is designed to detect finger motions in the proposed system. Independent component analysis (ICA) and grey relational analysis (GRA) are used to data reduction and the core of identification, respectively. Six features are extracted from each SEMG channel, and three features are computed from five bend sensors in the data glove. To test the feasibility of the system, this study quantitatively compares the classification accuracies of twenty hand motions collected from 10 subjects. Compared to the performance with a back-propagation neural network and only using GRA method, the proposed method provides equivalent accuracy (>85%) with three training sets and faster processing time (20 ms). The results also demonstrate that ICA can effectively reduce the size of input features with GRA methods and, in turn, reduce the processing time with the low price of reduced identification rates.

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