High-density force myography: A possible alternative for upper-limb prosthetic control

EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to develop myoelectric prosthesis. The performance of the prosthesis control-based applications mainly depends on extraction of eminent features with minimum neural information loss. The machine learning algorithms have a significant role to play for the development of Intelligent upper-limb prosthetic control (iULP) using EMG signal. This paper proposes a new technique of extracting the features known as advanced time derivative moments (ATDM) for effective pattern recognition of amputees. Four heterogeneous datasets have been used for testing and validation of the proposed technique. Out of the four datasets, three datasets have been taken from the standard NinaPro database and the fourth dataset comprises data collected from three amputees. The efficiency of ATDM features is examined with the help of Davies–Bouldin (DB) index for separability, classification accuracy and computational complexity. Further, it has been compared with similar work and the results reveal that ATDM features have excellent classification accuracy of 98.32% with relatively lower time complexity. The lower values of DB criteria prove the good separation of features belonging to various classes. The results are carried out on 2.6[Formula: see text]GHz Intel core i7 processor with MATLAB 2015a platform.

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High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control

Journal of Electromyography and Kinesiology ◽

10.1016/j.jelekin.2011.12.012 ◽

2012 ◽

Vol 22 (3) ◽

pp. 478-484 ◽

Cited By ~ 72

Author(s):

Heather Daley ◽

Kevin Englehart ◽

Levi Hargrove ◽

Usha Kuruganti

Keyword(s):

High Density ◽

Prosthetic Control

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High-density surface electromyography maps after computer-aided training in individual with congenital transverse deficiency: a case study

BMC Musculoskeletal Disorders ◽

10.1186/s12891-020-03694-4 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Katarzyna Kisiel-Sajewicz ◽

Jarosław Marusiak ◽

Mónica Rojas-Martínez ◽

Damian Janecki ◽

Sławomir Chomiak ◽

...

Keyword(s):

Spatial Distribution ◽

Upper Limb ◽

Trapezius Muscle ◽

High Density ◽

Emg Activity ◽

Average Intensity ◽

Mental Training ◽

Sensory Stimuli ◽

Computer Aided ◽

Before And After

Abstract Background The aim of this study was to determine whether computer-aided training (CAT) of motor tasks would increase muscle activity and change its spatial distribution in a patient with a bilateral upper-limb congenital transverse deficiency. We believe that our study makes a significant contribution to the literature because it demonstrates the usefulness of CAT in promoting the neuromuscular adaptation in people with congenital limb deficiencies and altered body image. Case presentation The patient with bilateral upper-limb congenital transverse deficiency and the healthy control subject performed 12 weeks of the CAT. The subject’s task was to imagine reaching and grasping a book with the hand. Subjects were provided a visual animation of that movement and sensory feedback to facilitate the mental engagement to accomplish the task. High-density electromyography (HD-EMG; 64-electrode) were collected from the trapezius muscle during a shrug isometric contraction before and after 4, 8, 12 weeks of the training. After training, we observed in our patient changes in the spatial distribution of the activation, and the increased average intensity of the EMG maps and maximal force. Conclusions These results, although from only one patient, suggest that mental training supported by computer-generated visual and sensory stimuli leads to beneficial changes in muscle strength and activity. The increased muscle activation and changed spatial distribution of the EMG activity after mental training may indicate the training-induced functional plasticity of the motor activation strategy within the trapezius muscle in individual with bilateral upper-limb congenital transverse deficiency. Marked changes in spatial distribution during the submaximal contraction in the patient after training could be associated with changes of the neural drive to the muscle, which corresponds with specific (unfamiliar for patient) motor task. These findings are relevant to neuromuscular functional rehabilitation in patients with a bilateral upper-limb congenital transverse deficiency especially before and after upper limb transplantation and to development of the EMG based prostheses.

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An Investigation on the Sampling Frequency of the Upper-Limb Force Myographic Signals

Sensors ◽

10.3390/s19112432 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2432 ◽

Cited By ~ 4

Author(s):

Zhen Gang Xiao ◽

Carlo Menon

Keyword(s):

Upper Limb ◽

Sampling Frequency ◽

Isometric Actions ◽

Monitoring Applications ◽

Minimum Requirements ◽

Data Content ◽

Acquisition Device ◽

Force Myography ◽

Machine Interface ◽

Insight Into

Force myography (FMG) is an emerging method to register muscle activity of a limb using force sensors for human–machine interface and movement monitoring applications. Despite its newly gained popularity among researchers, many of its fundamental characteristics remain to be investigated. The aim of this study is to identify the minimum sampling frequency needed for recording upper-limb FMG signals without sacrificing signal integrity. Twelve healthy volunteers participated in an experiment in which they were instructed to perform rapid hand actions with FMG signals being recorded from the wrist and the bulk region of the forearm. The FMG signals were sampled at 1 kHz with a 16-bit resolution data acquisition device. We downsampled the signals with frequencies ranging from 1 Hz to 500 Hz to examine the discrepancies between the original signals and the downsampled ones. Based on the results, we suggest that FMG signals from the forearm and wrist should be collected with minimum sampling frequencies of 54 Hz and 58 Hz for deciphering isometric actions, and 70 Hz and 84 Hz for deciphering dynamic actions. This fundamental work provides insight into minimum requirements for sampling FMG signals such that the data content of such signals is not compromised.

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A Review on Upper-Limb Myoelectric Prosthetic Control

IETE Journal of Research ◽

10.1080/03772063.2017.1381047 ◽

2017 ◽

Vol 64 (6) ◽

pp. 740-752 ◽

Cited By ~ 11

Author(s):

Nisheena V. Iqbal ◽

Kamalraj Subramaniam ◽

Shaniba Asmi P.

Keyword(s):

Upper Limb ◽

Prosthetic Control

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High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans

Scientific Data ◽

10.1038/s41597-020-00717-6 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Mónica Rojas-Martínez ◽

Leidy Yanet Serna ◽

Mislav Jordanic ◽

Hamid Reza Marateb ◽

Roberto Merletti ◽

...

Keyword(s):

Upper Limb ◽

Biceps Brachii ◽

High Density ◽

Isometric Contractions ◽

Myoelectric Activity ◽

Electrode Arrays ◽

Triceps Brachii ◽

Healthy Humans ◽

Limb Muscles ◽

Density Surface

AbstractThis paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as “bad” channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses.

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