A User Independent Method for Identifying

Hand Gestures with

sEMG

Hitoshi Tamura

, Kazuki Itou and Yasushi Kambayashi

Department of Robotics, Nippon Institute of Technology, Saitama, Japan

Keywords: Semg, Hand Gesture, Deep Learning, LSTM, Data Augmentation.

Abstract: We propose a method to determine hand gestures using sEMG (surface Electromyogram) measured from the

forearm. The detection method uses the LSTM (Long Short Term Memory) model of RNN (Recurrent Neural

Network). Although the conventional method requires the learning data of the user, this is a method that an

unspecified number of users can use immediately by enhancing the data. We have confirmed that the accuracy

does not change even if the mounting position of the sensor is shifted. We have shown the effectiveness of

the data enhancement by numerical experiments.

1 INTRODUCTION

People use hand gestures as a means of

communication. Even though we use speech and

facial expressions as the main means of

communication, we use hand gestures as part of

natural body language. In addition, we have

organized the hand gestures as sign language and

have been using it for conversation. Therefore, it is

natural to think that we may employ the hand gestures

as an interface to electronic devices and robots. In

order to have a machine recognize hand gestures, we

generally use two means. One is a method of using

computer vision technique. That is recognizing a

hand shape and its operation using imaging devices

such as cameras and depth sensors.

The other method is to acquire the shapes of the

fingers and three-dimensional acceleration through

sensors. In order to do so, we need to attach sensors

to the fingers. In the former, external sensors such as

cameras and depth sensors are required, and

restrictions such as shooting range and effective

distance are often imposed on the position of the

sensor. In the latter, the human movements are

restricted. In addition, the users have to endure from

wearing such devices. Both methods need to be

adjusted according to the shooting environment and

the individual when using them.

Many researchers have proposed variations of

both methods. They are called classical methods, and

are summarized in the reference (Mitra and Acharya,

https://orcid.org/0000-0002-7836-3042

2007). Information obtained from sensors and

cameras is used to classify gestures. We can utilize

various classification methods such as HMM (Hidden

Markov Model), FSM (Finite State Machine) and

PCA (Principal Component Analysis).

Recently, we have witnessed a remarkable

development of machine learning methods. Since the

machine learning methods have dramatically

improved the performance of the classifiers, many

identification problems have been solved. Especially,

many researchers have made neural networks

perform machine-learning through the measured

sEMG (surface Electromyogram) of forearms to

identify hand gestures

In this paper, we propose a method that identifies

hand gestures by classifying sEMG obtained from

forearms using commercially available sensors and

deep learning method. The sensors used are Myo

Gesture Control Armbands (hereinafter Myo)

manufactured by Talmic Labs (now North). They are

easy to attach and detach. We made eight sEMG

sensors connected like a bracelet so that they are

easily detachable from the forearm (Figure 1). The

signals acquired from the eight sEMG are transmitted

to a control device, i.e. a PC via the Bluetooth

connection. Each sensor equips one 3-axis

accelerometer and one 3-axis gyroscope. The weight

of this sensor is 93g and the thickness is 11 mm.

Therefore we can expect the users to feal little

discomfort when they wear. Myo is suitable for direct

operation in VR and AR.

Tamura, H., Itou, K. and Kambayashi, Y.

A User Independent Method for Identifying Hand Gestures with sEMG.

DOI: 10.5220/0009370103550362

In Proceedings of the 12th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2020) - Volume 1, pages 355-362

ISBN: 978-989-758-395-7; ISSN: 2184-433X

355

Figure 1: Myo Gesture Control Armband.

By using such simple sensors, we do not need to

install cameras, which are required in conventional

methods, nor attaching devices to fingers such as data

globes, which require wire connections and

adjustments. Furthermore, since mounting sEMG

devices imposes little discomfort to the users, we can

provide more natural interfaces.

For hand gesture analysis, several conventional

machine learning methods have been proposed for

learning sEMG. They are methods using decision

trees and HMM for sign language recognition (Zhang

et al., 2011), methods using decision trees and k-NN

(k-Nearest Neighbor) for hand gesture analysis (Lian

et al., 2017), methods using PCA for prosthetic

control (Matrone et al., 2011), methods using HMM

and SVM (Support Vector Machine) (Rossi et al.,

2015), and methods using an application of ANN

(Artificial Neural Network) to hand gesture analysis

(Liu et al., 2017).

The methods of classifying the pattern of sEMG

by machine learning are roughly divided into two

categories: one is dealing with static gestures and the

other is that of including dynamic gestures. For

dealing with only static gestures, it is sufficient to

analyze a few snapshots for some moments of sEMG.

In order to analyze general dynamic gestures,

however, it is necessary to obtain time series gesture

data.

In order to classify time series data of sEMG for

dynamic hand gestures, we use RNN (recurrent

neural network). RNN is suitable for time series data.

One particular RNN is especially suitable for time

series data. That is LSTM (long short term memory)

model. It is an extended version of RNN.

The idea of analyzing sEMG by RNN is not new.

It has been employed in the field of biomedical

engineering and robotics since 1990’s. It has been

used to estimate the angles of joints in a human body

from sEMG, and to calculate motor control

parameters that control robots, electric prosthetic feet,

power assist suits (Koike et al., 1993; Koike et al.,

1994; Koike et al., 1995; Cheron et al., 1996; Cheron

et al., 2003).

Applying LSTM to sEMG time series data to

classify gestures are found in (Wu et al., 2018;

Samadani et al., 2018; Quivira et al., 2018).

According to those experiments, LSTM improves the

accuracy of the classification.

The problem is that when using a simple sEMG

sensor such as Myo to identify the hand gesture, a

slight deviation of attaching the device greatly affects

the acquired values of sEMG. It is difficult to measure

the complex movement of the forearm muscles that

are complex three-dimensional shapes with a sensor

that can measure only the muscle potential of the

body surface. Conventionally, this problem is

avoided by providing a large amount of data for

machine-leaning. However, this method requires a

large amount of data for each user, and the learned

classifier is effective only for that particular user. It is

difficult to make classifiers ready for unspecified

number of users.

There are many proposals to classify sEMG by a

classifier that is built by machine learning. However,

most of them are tailored for a specific user. Because

they are built from the user's own learning data. A

user has to provide a large amount of his or her own

data for the machine learning. There is no known

attempt of trying to build a learned classifier for

unspecified number of users.

In this paper, we report our attempt to develop a

hand gesture classifier that can be applied to an

unspecified number of people by effectively

augmenting several sEMG data.

2 TARAGET GESTURES AND

DETERMINATION METHODS

2.1 Types of Target Gestures

Figure 2 shows the target gestures. We classified six

types of gestures (weakness, paper, lightly grasping,

strongly grasping, pointing finger, and scissors).

We measured each gesture, and took for each four

seconds. Since we wanted to have practical setting,

we did not exclude the duration time sEMG being

stabilized. We started to measure sEMG for four

seconds when the user started to perform each

gesture.

Senso

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Figure 2: Hand Gestures to measure.

2.2 Configuring LSTM Networks

We used SONY's Neural Network Console (NNC) as

an integrated development environment for deep

learning. We have fine-tuned the LSTM included in

the NNC sample and used for learning. The LSTM

that we used for learning has three gates (Input Gate,

Output Gate, and Forget Gate) in the hidden layer.

The hidden layer is called LSTM Block. Owing to

this hidden layer, the LSTM exhibits high

discriminant performance for time series data. Figure

3 shows the structure of LSTM, and Figure 4 shows

the network that was actually used for learning.

Figure 3: LSTM Structure.

2.3 Method for Obtaining Learning

Data

In this study, we constructed a system that records

sEMG data obtained from Myo and stores in a CSV

file for every 10ms (Figure 5). The digital values of

Figure 4: Network Used for Learning.

Input Layer Output Laye

A User Independent Method for Identifying Hand Gestures with sEMG

357

the acquired sEMG are output in the range of -127 to

127. In order to learn in the NNC, however, we had

to convert the data into the normalized data ranged -1

to 1 before outputting to the CSV file. Figure 6 shows

an example of the output CSV file .

Figure 5: Measurement system.

Figure 6: Example of a measured CSV file.

2.4 Data Augmentation

We added some random noise data range of -0.15 to

0.15 to each measured sEMG datum. Further

augmented data smoothed by taking the average value

for every 20ms. We then generated ten new data with

random noise from one measurement datum and

added to the original data. We made learning be done

with dataset eleven times as the original data.

Figure 7: Measured sEMG data.

Figure 7 and 8 show an example of the data measured

by sensor No. 0 and the corresponding newly

generated data by adding noise respectively.

Figure 8: sEMG data with augmented processing.

3 EXPERIMENT

3.1 Accuracy of Reattaching Myo

The author measured his own learning data (2,880).

When we measured Myo continuously for learning

data and evaluation data without attaching and

detaching, the accuracy was 96.38%. On the other

hand, when we measured Myo for learning data,

detached Myo, and then measured the evaluation data

by attaching the same position as the previous time as

much as possible, the accuracy of the evaluation data,

which were determined by the previous learned

parameters, was 35.00%. We can observe that the

accuracy decreased significantly by the deviation at

the time of mounting.

We measured the learning and evaluation data by

re-wearing Myo at seven angles deliberately shifted

by 15 degrees for the deviation. We made the

machine learn with a total of 22,680 data. The

evaluation data were data measured after re-wearing

Myo. As a result, we found 93.3% accuracy even

when there was a deviation of re-wearing Myo. From

this, it can be said that it is possible to prevent the

decrease of the accuracy due to deviation at the time

of mounting, by mounting it in several places.

Table 1 shows the evaluation results when the

learning data was measured by wearing Myo at seven

angles. The accuracy shown in Table 1 is the

percentage of correctly determined in all inferences.

The precision is the rate of correctly determined and

estimated to be true. The recall is the percentage of

estimated to be true if the data is true. The F-measures

is the harmonic mean of the precision and the recall.

Table 1: Evaluation results of learning.

Accurac

93.3%

Precision 94.2%

Recall 93.3%

F-Measures 93.3%

Time

[

]

Time [ms]

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3.2 Gesture Classification on Different

Subject's sEMG

We have investigated whether an unspecified number

of users can use the current learned parameters. We

have employed three new subjects, and repeated the

measurement 60 times. We have used the previously

learned parameters described above, and determined

with the data of three people. As a result, the average

of the correct answer rate of the three people

significantly decreased to 43.89%. Table 2 shows the

discriminant results.

Table 2: Determination results for each subject.

Gesture

Recall of

Subject A

Recall of

Subject B

Recall of

Subject C

40.0% 0.0% 70.0%

lightly

Grasping

20.0% 20.0% 0.0%

Strongly

Grasping

80.0% 50.0% 80.0%

Pointing

Fin

80.0% 60.0% 100%

Scissors 60.0% 0.0% 0.0%

weakness 70% 60.0% 0.0%

Average 58.3% 31.7% 41.7%

When examining the gestures individually, the

"strongly grasped" and "pointing finger" gestures

provided a high accuracy regardless of the

individuals, but "lightly grasping" and "scissors"

gestures hardly provided any accuracy. Figures 9 and

10 show examples of sEMG measurement data for the

"lightly grasped" and "scissor" gestures. We can

observe that the signals created by "lightly grasping"

and "scissors" actions display variety of wave forms.

They clearly differ from each other in the way of

applying muscle power. It seems that each different

individual applies his or her muscle power for "lightly

grasping" and "scissors" in a quite unique way.

3.3 Results of Data Augmentation with

Random Noise

In order to find out whether the accuracy can be

improved by the data augmentation, we have acquired

additional data. The subjects were asked to wear Myo

at three angles and we measured 3,240 data. In the

experiments, we added some random noise data to

these collected data, and augment the number of data

eleven times as many as the original ones, and made

perform learning with 35,640 data. We measured

another set of 3,240 evaluation data in the same

manner as the learning data. As a result of learning

only the measured data, the accuracy was 75.03%.

The accuracy of learning that augmented the data was

78.48%. In other words, the accuracy improved by

3.45%.

We observed a little improvement of the accuracy.

Even though for some gesture, we observed a case

where learning with only measurement data showed

higher accuracy than learning with augmented data,

in general, learning with augmented data displays

better accuracy. Table 3 shows the discriminant

accuracy of each gesture of the classifier learned only

by the measurement data and the classifier learned

using the augmented data.

Figure 9: Lightly Grasp (Upper: Learning Data, Lower:

Subject A).

Sensor No

Time

A User Independent Method for Identifying Hand Gestures with sEMG

359

Figure 10: Scissors (Upper: Learning Data, Lower: Subject

A).

Table 3: Changes in detection rates due to data

augmentation.

Gesture

Recall of

Measured Data

Recall of

Augmented Data

Pape

61.7% 50.0%

lightly

Grasping

82.6% 77.0%

Strongly

Gras

94.1% 96.7%

Pointing Finger

85.9% 64.8%

Scissors 31.5% 82.4%

weakness 94.4% 100.0%

Table 4 and Table 5 show the evaluation results by

each classifier.

Table 4: Evaluation Results for Measurement Data only.

Accurac

75.0%

Precision 77.3%

Recall 75.0%

F-Measures 74.0%

Table 5: Evaluation Results with augmented Data.

Accurac

78.5%

Precision 80.4%

Recall 78.5%

F-Measures 78.0%

4 DISCUSSION

By attaching Myo and changing the angle several

times, we could improve the accuracy even if the

subjects re-wear Myo. The reason for this

phenomenon might be the leaning does not only

depends on the value of each sEMG sensor, but also

it depends on the numerical balance of the eight

sEMG sensors. By learning the data with unfixed

angle, we could avoid the over-fit that depends on a

specific sensor.

We have found that different individuals provide

different output values of sEMG even with the same

gesture. In order to avoid overfitting individual-

dependent features, it is necessary to measure data

from a large number of people. The need to collect

data from many people is clear from the result that the

accuracy decreased to 43.89% when we applied one

specific person's data to others.

On the other hand, the method of changing the

angle while measuring requires a large number of

repetitions of measurement, thus requires an

extremely long measurement time. In order to

measure data efficiently, data expansion is essential.

With parameters learned by a specific individual’s

data, the accuracy becomes very low as determining

the gesture of another person. One of the reason is that

the way of the muscle power applied to the fingers of

each person is different even in the same gesture

movement. For example, in the case of the "scissors"

gesture, some people don't put any muscle force on

their thumbs, and some other people put their thumb

and ring finger on top of each other. Even performing

the same gesture, there are different patterns in which

applying the muscle force. It is difficult to determine

the gesture in such cases.

The second reason is that the effect of individual

differences in the muscle strength of the entire hand.

Even with the same gesture, the condition of applying

muscle force to the whole hand is different. Although

Time

Sensor No

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sEMG may be able to solve this by normalizing the

width between the maximum value and minimum

value of the measured value, it may be difficult to

distinguish between the state of straining muscle and

the state of relax. Therefore, to grasp the force level

of each subject in advance, it is necessary to match

the process of some criteria. For example, we can

divide the power levels into three stages of weak,

medium, and strong, and then instruct the subjects to

gesture at the level of "medium".

In machine learning, by collecting and learning

data from a large number of subjects, it should be

possible to generate a classifier that is not affected by

individual differences, such as strength of force and

differences in finger usage. However, it is too

expensive to collect a large amount of data that needs

to be physically measured. Therefore, data

augmentation is also important in this perspective.

In this data augmentation, we have added some

noise data directly to the sEMG sensor measurement

data. However, we are planning to add random noise

only to the fine features that is maintaining the

characteristics of frequency spectrum envelope just as

the analysis method of the audio signal. With this new

data extension method, it may be possible to generate

artificial data with similar characteristics to the

measurement data. Although not included in this

paper, the preliminary experiments suggest that a new

data extension method is effective.

5 CONCLUSION

Hand gestures are not only providing a means of

communication between people, but also attracting

attention as a method for operating electronic devices

and robots. Conventional recognition method using

computer vision requires camera and method using

sensors requires wearing glove-type devices.

In recent years, the performance of the classifier

by machine learning method such as deep learning

has been improved. Therefore, there are many studies

that try to improve the discriminant accuracy by

learning sEMG of hand gestures. In this study, we

measured sEMG using the armband type device Myo,

which is easy to attach and detach, and learning by

the network of LSTM model of RNN, and

experimented with the method of determining the

hand gestures for an unspecified number of subjects.

We have performed the following experiments.

1. Discriminant accuracy by wearing Myo with

deviation.

2. Discriminant accuracy in classifiers learned

by data measured by deliberately shifting

angles.

3. Expansion of learning data by random noise.

The summary of the experimental results are as

follows.

1. By learning with the data measured by

wearing Myo from multiple angles, the

inaccuracy due to the wearing deviation is

reduced, and robustness is improved.

2. As the data extension, the improvement of

the discriminant accuracy can be expected

by adding noise.

As a future direction, we will try to reduce the

influence of individual muscle force by measuring at

the strength level of weak, medium, and strong. In

regard to the data augmentation, we are planning to

develop an interface that can be used by anyone with

minimal adjustment by trying a method to generate

similar to artificial data that maintains the

characteristics of spectral envelope.

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