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European Journal of Applied Sciences – Vol. 10, No. 6

Publication Date: December 25, 2022

DOI:10.14738/aivp.106.13526. Kitagawa, K. (2022). Prediction Method for Large Grasping Force using Single-Channel Surface Electromyography: A Pilot Study.

European Journal of Applied Sciences, 10(6). 320-325.

Services for Science and Education – United Kingdom

Prediction Method for Large Grasping Force using Single-Channel

Surface Electromyography: A Pilot Study

Kodai Kitagawa

Mechanical and Medical Engineering Course

Department of Industrial Systems Engineering

National Institute of Technology, Hachinohe College, Japan

ABSTRACT

Frequent grasping with a large force in the workplace leads to work-related

musculoskeletal disorders (WMSDs). Thus, the frequency and force of grasping in

each worker should be monitored for preventing these WMSDs. The frequency of

grasping can be counted by observation and existing methods using vision-based

devices or wearable sensors. Grasping force measurement generally requires force

sensors between the hand and objects. Almost force sensors are limited to the

surface and shape of the object in measurement; thus, measurement method

without force sensors is necessary to monitor grasping force in various workplaces.

Previous studies developed measurement methods for grasping force using

electromyography (EMG). However, almost existing methods require multiple- channels EMG for grasping force measurement. In addition, many methods were not

applied to large grasping forces. Therefore, the objective of this study is to develop

the prediction method for large grasping force using single-channel EMG. The

proposed method predicts grasping force by machine learning and features

calculated from single channel surface EMG (sEMG) signal on flexor digitorum

superficialis (FDS) muscle. As a pilot study, the proposed method was tested by

sEMG data of different large grasping forces (5 to 60 kgf). The results showed that

the correlation between the actual and predicted grasping force was greater than

0.8. These results indicate that the proposed method could measure large grasping

force by only single-channel sEMG.

Keywords: Grasping force; Surface electromyography; Occupational health; Machine

Learning.

INTRODUCTION

Grasping in the workplace leads to work-related musculoskeletal disorders (WMSDs) [1–3].

Especially, grasping tasks with high frequency and large force are known as a risk factor for

carpal tunnel syndrome (CTS) in occupational health [1]. In addition, there were high

prevalence ratios for WMSDs in high-frequency grasping with more than 4.0 kgf [2].

Furthermore, Shanahan et al. reported that grasping effort is an effective factor to evaluate

WMSDs risk in the upper limbs [3]. From these findings, it is considered that the frequency and

force of grasping in each worker should be monitored for preventing these WMSDs.

The frequency of grasping can be counted by observation and existing methods using vision- based devices or wearable sensors such as accelerometers, strain sensors, or electromyography

[4–6]. On the other hand, grasping force measurement generally requires force sensors

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Kitagawa, K. (2022). Prediction Method for Large Grasping Force using Single-Channel Surface Electromyography: A Pilot Study. European Journal

of Applied Sciences, 10(6). 320-325.

URL: http://dx.doi.org/10.14738/aivp.106.13526

between the hand and objects [7,8]. However, common force sensors are limited for the surface

and shape of the object in measurement [9]. Thus, new measurement methods without force

sensors are necessary. Previous studies developed measurement methods for grasping force

using electromyography (EMG) [10–16].

Table 1 shows the related studies of grasping force prediction using EMG [10–16]. Table 1

shows that the applied grasping forces of 4 related studies were less than 100 N (approximately

10 kgf) [11,12,14,15]. In other studies, applied grasping forces were larger than 100 N [10,13].

Existing methods of these studies might be used for preventing WMSDs due to large grasping

force; however, these studies required multiple-channels EMG [10,13]. Single-channel EMG

might be better for useful, comfortable, and continuous application in WMSDs prevention. On

the other hand, Kamavuako et al.’s study provided grasping force prediction using single- channel EMG in 2009 [15]. However, as mentioned previously, large grasping forces larger than

100 N were not applied in this study [15]. In addition, Kamavuako et al.’s further study provided

prediction method using single-channel EMG for grasping force larger than 100 N [16].

However, it is difficult to use this method for monitoring application because this method

requires invasive intramuscular EMG [16].

From these backgrounds, the objective of this study is to develop the prediction method for

large grasping force using single-channel non-invasive surface EMG (sEMG).

Table 1. Related Studies of Grasping Force Prediction using EMG

Reference Electrode Number of

Channel

Applied Grasping

Force [N]

Wu et al. [10] Surface 3 to 6 Maximum: 312.7

Kim et al. [11] Surface 6 Maximum: 40

Martinez et al. [12] Surface 4 to 16 Maximum: 31.1

Yang et al. [13] Surface 8 Maximum: 467

Ma et al. [14] Surface 16 Maximum: 92

Kamavuako et al. (2009)

[15] Surface Single Maximum: 50

Kamavuako et al. (2013)

[16] Intramuscular Single Mean ± S.D.: 481 ± 69

Proposed Method

Figure 1 shows the proposed method for predicting large grasping force using single-channel

sEMG. The proposed method predicts grasping force by machine learning and features

calculated from single channel sEMG signal on flexor digitorum superficialis (FDS) muscle. The

relationships between the grasping and FDS muscle were reported by many previous studies

[17–20]. Features (mean, median, maximum, minimum, S.D., variance, kurtosis, and skewness)

for machine learning-based regression were selected from related studies using machine

learning [21,22]. As a pilot study, this study evaluated whether the proposed method could

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European Journal of Applied Sciences (EJAS) Vol. 10, Issue 6, December-2022

Services for Science and Education – United Kingdom

predict large grasping force by only single-channel sEMG and machine learning. Furthermore,

artificial neural network (ANN), support vector machine (SVM), k-Nearest Neighbor (kNN),

gaussian process, linear regression, and M5P were compared to find a suitable machine

learning algorithm for regression of the proposed method.

Figure 1. Grasping Force Prediction

EXPERIMENT

This study evaluated whether the proposed method could predict large grasping force by only

single-channel sEMG and machine learning The participant of the experiment was one young

male (28 years, 189 cm, 168 kg). The participant provided informed consent to this experiment.

The participant performed grasping the adjustable hand-gripper with different grasping forces.

The posture of the participant was defined in Figure 1. Grasping forces were set as 5, 10, 20, 30,

40, 50, and 60 kgf by the adjustable hand-gripper. The participant performed 10 trials of

grasping for each grasping force. Grasping of each trial was kept for 5 seconds. The order of

trial and grasping force was randomized. Note that there was 2 minutes rest time after each

trial. Muscle activity of the FDS muscle during grasping was measured by surface EMG (EMG- EYE2, Global Linx Technology Co.,Ltd., Japan). The sampling rate of sEMG was 500 Hz.

Measured sEMG signals were processed via full-wave rectification. Features (mean, median,

maximum, minimum, S.D., variance, kurtosis, and skewness) for machine learning were

calculated from processed sEMG signals. These signal processing were performed by MATLAB

R2020b (Mathworks, Inc., Natick, MA).

Machine learning algorithms for the proposed method (ANN, SVM, kNN, gaussian process,

linear regression, and M5P) were implemented and tested in WEKA [23]. Training and testing

of machine learning were performed via leave-one-out cross-validation (LOOCV). Correlation