Investigation of the Linear Relationship between Grasping Force and Features of Intramuscular EMG

Bøg, Mette Frydensbjerg; Erkocevic, Ema; Niemeier, Marko Jörg; Mathiesen, Jakob Rosenvang; Smidstrup, Anne; Kamavuako, Ernest Nlandu

doi:10.1007/978-3-642-21683-1_30

Cited by 10 publications

(8 citation statements)

References 4 publications

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“…So far researchers have investigated this relationship using either intra-muscular [9][10][11][12] or surface EMG recordings [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Several estimation techniques, exploiting a wide range of methodologies, were proposed (Table 1).…”

Section: Introductionmentioning

confidence: 99%

“…By using several different algorithms, such as regressors [9,11,16,17,22,24,25] or artificial neural networks [9,[11][12][13][14]21,26,27], these studies demonstrated notable estimation accuracies up to 0.95 R 2 (coefficient of determination) or 4.21% absolute error (AE) from wrist, finger and trunk movements, and from a wide range of forces (e.g. from 0% to 100% of muscle activation [10,11,13,[15][16][17]19,22,23,25,26], or up to 300N of output force [9,12,14,18,24]). Notably, some of the methods developed for the estimation of the grip force from the EMG also allow for the simultaneous control of up to 6 degrees of freedom of a prosthesis [12,15,17,20,25,27].…”

Section: Introductionmentioning

confidence: 99%

“…This indeed was demonstrated to be descriptive of the intended movement [31]. However, thus far, such temporal arrangement was poorly exploited for the estimation of the output force during a movement (Table 1, [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]). Here we hypothesized that the informative content of the transient EMG in humans is likely due to the preplanned nature of grasping, in general, and of the final GF, in particular [32].…”

Section: Introductionmentioning

confidence: 99%

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Grasp force estimation from the transient EMG using high-density surface recordings

et al. 2020

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Objective. Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis control are among the major challenges in applied neuroscience and bioengineering. Usually, information from the electrical activity of residual forearm muscles (i.e. the electromyogram, EMG) is used to control different functions of a prosthesis. Noteworthy, forearm EMG patterns at the onset of a contraction (transient phase) have shown to contain predictive information about upcoming grasps. However, decoding this information for the estimation of grasp force (GF) was so far overlooked. Approach. High density-EMG signals (192 channels) were recorded from twelve participants performing a pick-and-lift task. The final GF was estimated offline using linear regressors, with four subsets of channels and ten features obtained using three channels-features selection methods. Two different evaluation metrics (absolute error and R 2), complemented with statistical analysis, were used to select the optimal configuration of the parameters. Different windows of data starting at the GF onset were compared to determine the time at which the GF can be ascertained from the EMG signals. Main results. The prediction accuracy improved by increasing the window length from the moment of the onset and kept improving until the steady state at which a plateau of performances was reached. With our methodology, estimations of the GF through 16 EMG channels reached an absolute error of 2.52% the maximum voluntary force using only transient information and 1.99% with the first 500 ms of data following the onset. Significance. The final GF estimation from transient EMG was comparable to the one obtained using steady state data, confirming our hypothesis that the transient phase contains information about the final GF. This result paves the way to fast online myoelectric controllers capable of decoding grasp strength from the very early portion of the EMG signal.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Grasp force estimation from the transient EMG using high-density surface recordings

et al. 2020

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“…bilgi dogrultusunda, bireyin tabi tutuldugu göreve baglı olarak kas etkinligi seviyesindeki degişimi yansıtan karesel ortalama yöntemi uygulanarak yEMG sinyalinden dört farklıöznitelik elde edilmiştir [12].İşlenmiş yEMG sinyalinin 3000. ve 5000. orneklerini içeren (5.9-9.8 s) bir pencere içindeki en büyük, toplam ve enerji degerleri ile, sinyalin en büyük degerini merkez alan ve 1500örnek komşuluklarını kapsayan bir pencerenin enerjisi hesaplanmış ve yEMGöznitelikleri olarak kullanılmıştır. C.İLİNTİ ANALİZİ yEMG ve görev zorlugu arasındaki iyi bilinen ilişkinin ışıgında, EEG ve yEMGöznitelikleri arasında yapılacak bir ilinti analizi, sınıflandırma sonuçları ile elde ettigimiz EEG'nin istek düzeyi bilgisine sahip oldugu çıkarımını destekleyecek bir ek çalışma olacaktır.…”

Section: B Yemg Veri̇ Anali̇zi̇unclassified

Detection of task difficulty from intention level information in the EEG features

Koyas¹,

Hocaoğlu²,

Çetin³

et al. 2014

2014 22nd Signal Processing and Communications Applications Conference (SIU)

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Ozetçe -Bu çalışmada, gönüllülerin deneydeki görev zorluklarına baglı olarak gösterdikleri istek düzeylerinin tespitini yapan elektroensefologram (EEG) tabanlı bir beyin-bilgisayar arayüzü (BBA) yaklaşımı sunuyoruz. Bunun için farklı agırlıklardaki yükler sag elde kaldırılırken dirsek bükmesi ve takiben gevşetilmesi hareketinin yapılması sırasında oluşan olgu ile ilgili eşzamanlanma ve eşzamanlanma bozulumu desenleri dogrusal ayırtaç analizi (DAA) ile sınıflandırılmaktadır. Sonuçlarımız degişen zorluklardaki görevlerin EEG sinyallerine dayalı olarak sınıflandırılabildigini göstermektedir. Ayrıca, EEG ve yüzey elektromiyogram (yEMG) sinyallerinden tespit edilen istek düzeyleri arasında bir ilinti analizi sunulmaktadır ve iki sinyal arasında tespit edilen belirli seviyedeki dogrusal ilişki sonuçlarımızı desteklemektedir. Fiziklel rehabilitasyon tedavileri sırasında hastaların istek seviyelerini belirleyebilmek hastanın terapideki görevine daha aktif katılımını saglamakta ve robotik rehabilitasyon sistemlerinin etkinligini arttırmaktadır. Bu nedenle,önerdigimiz tipte bir istek düzeyi belirleme yaklaşımının bu tür fiziksel rehabilitasyon süreçlerinde faydalı olma potansiyeli mevcuttur.Anahtar Kelimeler-EEG, BBA, yEMG, istek düzeyi, rehabilitasyon Abstract-In this study, an approach which detects the level of intention in response to the difficulty of the task executed by the subjects in an electroencephalogram (EEG) based braincomputer interface (BCI), is proposed. For this purpose, event related synchronization and desynchronization patterns which occur in the process of lifting different weights by the right hand by executing elbow flexion and extension movements, are classified by the linear discriminant analysis (LDA). Our results show that the varying difficulty of the task can be classified based on the EEG signals. In addition, a correlation analysis between the intention levels detected from EEG and surface electromyogram (sEMG) signals is presented and the detected level of correlation between these two signals supports our previous inference. Determining the level of intention of the patients during the physical rehabilitation treatment, ensures the patients' active participation in their therapy task and increases the effectiveness of the robotic rehabilitation system. Accordingly, the type of intention level detection approach we propose here has the potential to be useful in such physical rehabilitation processes.

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“…However, it was proven by several groups that in humans there is no linear relationship between the EMG signals and the generated GF [4]. Using intramuscular EMG recordings and non-linear methods, it was possible to relate the EMG signals of various muscles with their force output [5], [6]. Specifically, the latter was found to be modulated by the number of recruited motor [4], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Grasp Force Estimation from HD-EMG Recordings with Channel Selection Using Elastic Nets: Preliminary Study

Martínez

Clemente

Kanitz

et al. 2018

2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)

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The force applied with a prosthetic device is fundamental for the correct handling of objects in daily tasks. However, it is also a factor that normally gets relegated to a secondary plane, as researchers mainly focus on decoding the users intent in terms of movements to be performed. Continuous estimates of the grasp force from the electromyographic (EMG) signals were proposed in the past. As motor actions are preplanned in humans, we hypothesized that it would be possible to decode the intended grasp force from the transient state of the EMG signal. We tested this hypothesis by using features extracted from surface HD-EMG recordings from forearm muscles, classified using artificial neural networks. Data from 6 able-bodied subjects were collected. They were trained and tested at segments of 120 ms with 20 ms overlap, starting 1 s before and ending 0.5 s after the detection of the onset with different subsets of channels. The results obtained showed that the transient phase contains information about the target grasp force, achieving predictions of 2.62 % MVC average absolute errors within 430 ms from the onset of the EMG.

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Investigation of the Linear Relationship between Grasping Force and Features of Intramuscular EMG

Cited by 10 publications

References 4 publications

Grasp force estimation from the transient EMG using high-density surface recordings

Grasp force estimation from the transient EMG using high-density surface recordings

Detection of task difficulty from intention level information in the EEG features

Grasp Force Estimation from HD-EMG Recordings with Channel Selection Using Elastic Nets: Preliminary Study

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