Algorithm for Onset/Offset Detection of EMG Signals for Real-time

Control of a Low-cost Open-source Bionic-hand

Sandra Rodrigues

and Milton P. Macedo

1,2 a

Instituto Politécnico de Coimbra, ISEC, DFM, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal

LIBPhys, Department of Physics, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal

Keywords: Electromyography (EMG), Onset/Offset Detection, Algorithm, Real-time Control.

Abstract: This work was carried out as part of a project to develop a low-cost open source bionic hand using

electromyographic (EMG) signals. Probably the most important task for the success of this bionic hand is to

achieve a correct determination of muscle activation intervals. In this paper it is presented an algorithm for

the detection of Onset/Offset to be executed in an Arduino UNO. The aim of this algorithm is to be executed

in this ATmega328 microprocessor with a 16 MHz clock speed and 32 kBytes of memory in order to

accomplish with effectiveness the real-time control of the bionic hand. The tests performed up to its

application in real-time detection of muscle activation will also be described. The preliminary results

presented show a 100% success rate in most gestures performed by the bionic hand but with the occurrence

of a few false activations.

1 INTRODUCTION

With the purpose of using EMG signals for the

control of a bionic hand there is an absolute need of

an algorithm that is able to make a correct

identification of time windows in which the muscle

activation occurs. The hardware platform is

continuously acquiring a EMG signal and it is

demanded that in real-time, i.e., as soon as possible,

following the muscle deactivation, it is detected.

Additionally it has to save the data regarding the

whole time interval of muscle activation in some way

in order to afterwards complete the execution of the

correct action. This work is focused on the

implementation of one algorithm for onset/offset

detection but facing these constraints, with the aim of

using a low-cost microcontroller, Arduino Uno, as a

standalone controller of a bionic hand. Regarding this

real-time application, in a certain sense, the requisites

for the algorithm are more demanding, as well as

limitations in memory space and processing speed of

the hardware platform, has to be considered.

Several methods are available in literature for

onset/offset detection, i.e., for the determination of

time intervals in which the muscle is active, using

different definitions of thresholds to find the

https://orcid.org/0000-0003-0595-5298

beginning and end of a muscle activation, considering

a single threshold of signal amplitude, based on a

deviation from the baseline of three times the

standard deviation (Di Fabio, 1987), or using a double

threshold (Bonato, 1998). It is also described in

literature a method that detects muscle activity onset

using the energy of the signal which increases with

the start of the contraction (Rasool, 2012).

However these methods are general purpose in the

sense that the acquisition of EMG data is performed

previously. The need to carry out real-time control of

the bionic hand imposes a number of additional

requirements also on algorithms for data acquisition,

particularly in respect to onset/offset detection. In the

literature there are examples of algorithms developed

for similar applications, but in some cases it is

required that the processed signal is known a priori.

To implement an algorithm independent of the a

priori knowledge one option was to focus onto the

Teager-Kaiser Energy Operator (TKEO), which puts

in evidence the instantaneous increase of the action

potential and reduces the baseline noise (Li, 2007)

(Gentile, 2017). A threshold algorithm was then

implemented in TKEO’s domain for detecting muscle

activity, taking into consideration the minimum

period of muscular activity, the minimum period of

muscle inactivity and the margin of accuracy in the

872

Rodrigues, S. and Macedo, M.

Algorithm for Onset/Offset Detection of EMG Signals for Real-time Control of a Low-cost Open-source Bionic-hand.

DOI: 10.5220/0010976500003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 872-878

ISBN: 978-989-758-552-4; ISSN: 2184-4305

estimation of such measures (Konrad, 2006) (De

Luca, 1997).

Another paper concerns with the acquisition and

analysis of EMG signals for multiple active hand

movements based on wrist-hand mobility for control

of prosthesis robotic hand (

Raurale, 2014). An EMG

hardware module had been developed but the

classifier based on Linear Discriminant Analysis

(LDA) with threshold detection approach, resulted in

a very low processing time for the pattern recognition.

In spite of the documented success of some of

these methods, the algorithm presented in this paper

is based on a previous work in which a smoothing

filter is applied to EMG signal (Russo, 2019). These

filtering is achieved through a derivative component

that evidences the largest variations discarding the

signal noise. These enables the more efficient

application of a single threshold as higher frequencies

of the signal were suppressed.

2 METHODS

The algorithm was initially tested in Matlab using

previously acquired data from EMG signals. Later,

the code had to be adapted in order to be executed in

an ongoing data acquisition. To face the difficulty in

carrying out its debug, there was an intermediate step

of executing the algorithm in Arduino Uno through

the Arduino IDE platform with visualization of the

acquired signals in Opensignals. At this stage, for the

sake of ease in debugging, signals were acquired from

the accelerometer module of BITalino, whose shape

it was possible to adjust in a simple way. Through

Arduino's serial monitor, it was possible to follow the

values of some flags used for debugging, which were

complemented by the visualization of signals in

Opensignals. At this stage, the accelerometer signals

were acquired in parallel by Arduino UNO and a

BITalino. Through these, it was possible to improve

the code, identifying and correcting errors in the

algorithm and debugging the code until the conditions

for the independent execution of the code in Arduino

UNOo were created.

3 ALGORITHM

The initial code of the algorithm was developed in

Matlab, based on data smoothing previously

described in the literature (Russo, 2019). Three

parameters were defined that must be previously

adjusted in order to optimize the efficiency of

onset/offset detection. These parameters together

with auxiliary flags allow to control the flow of

execution of the different routines.

Figure 1 shows the data of an EMG signal

acquisition in which three muscle activations are

identified. Green and red markers corresponding to

onset and offset detection, respectively, are also

represented. These markers are obtained from each

raw-data from EMG sensor, which is computed in

order to calculate the difference from the previous

value. The average of an amount of these values

defined by the parameter arraySize is the key value

for the application of a threshold, defined by a second

parameter, activation, from which it is compared in

order to detect firstly the onset in upward direction

and offset in downward direction.

These markers do not delimit muscle activation,

and it is necessary to establish the time when

activation starts, with the help of First_onset_flag, as

well as the time when muscle activation ends. In this

case, Last_offset_flag is used, defining a period of

time that must elapse since the previous offset, using

the third parameter, DesactDelay. The description of

the various flags used for this flow control is

presented in table 1.

Figure 1: EMG signal acquisition with onset and offset

markers, green and red, respectively.

The parameter arraySize imposes the degree of

smoothness as it raises when the average is calculated

over a larger amount of values of EMG signals.

Consequently it also influences the sensitivity of the

algorithm in onset/offset detection as a greater

smoothing of the signal reduces its fluctuations and

the number of times it crosses the threshold value.

The activation parameter, on the other hand, has a

direct action in the detection of the onset/offset, as a

lower threshold value implies a more frequent

recognition of a variation as an onset, that is, it

increases the sensitivity of the algorithm. It is

important to be clear that this threshold is applied to

the smoothed signal instead of raw EMG signal. In

EMG signal amplitude (a.u.)

Algorithm for Onset/Offset Detection of EMG Signals for Real-time Control of a Low-cost Open-source Bionic-hand

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Table 1: Description of the flags used for flow control.

Description

Init_flag

To avoid onset detection for the first data acquisition owing to a large

difference to previous value

State_sign Identification that an onset is already active

First_onset_flag Signals the occurrence of a first onset and starts time counting

Last_offset_flag

Activated when the defined time interval has elapsed from the last offset with

no onset between

Figure 2: Overall schematic of the algorithm.

short, these two parameters act to detect the

onset/offset that occurs multiple times during a

muscle activation. On the other hand, the

DesactDelay parameter will only act to identify the

end of muscle activation, as it sets the time that will

have to elapse after an offset, without the following

onset, so that it is considered as the end of muscle

activation. Therefore, it also influences the

determined instant for muscle relaxation.

Figure 2 illustrates the overall schematics of the

algorithm from the initializations of the flags listed in

Table 1 through the specific code in the first time slot

in order to avoid false activation and the function to

calculate the smoothed values to the function for

onset/offset detection that is described in detail in

Figure 3.

Figure 4 shows the muscle activation intervals

obtained with this algorithm for the acquisition of

EMG signals shown in Figure 1.

As can be seen, the values of the three parameters

are shown in the figures. To illustrate the effect of the

DesactDelay parameter, through the observation of

figure 5, it can be seen that decreasing its value by

100 milliseconds (250 instead of 350), the instant of

time obtained for muscle relaxation then shifts to the

left. In this case, it has no other effect, namely in

determining the moment of muscle activation and

sensitivity in detecting onset/offset.

But with smaller values of this parameter, several

false activations can be identified within a single

muscle activation. It is important to adjust this value,

taking into account the type of signal that is acquired,

namely the frequencies present in that signal.

So, for example, when an application is made for

the acquisition of accelerometer signals, this

parameter must be the used with a different value than

from EMG signals.

Figure 6 is intended to illustrate the effect of the

other two parameters. In (a) and (b) the arraySize

parameter has been reduced by 100 units, that is, the

signal smoothing is weaker, since the average value

of the differences between consecutive values of the

EMG signal is calculated for a smaller amount of

values.

As a consequence, fluctuations in this mean value

increase, making the algorithm more sensitive to

fluctuations in the EMG signal. This fact is

responsible for the identification of a false muscle

activation.

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Figure 3: Schematic of the function OnsetOffsetDetection of the algorithm.

Figure 4: EMG signal acquisition with lines showing the

start and end of muscle activation, green and red,

respectively.

In (c) and (d) it is possible to observe the effect of

reducing the activation parameter value, which is the

threshold value, i.e., the minimum value of the

smoothed EMG signal to be considered as an onset.

The time interval of muscle activation has no

variation, but there are several false activations, as

this threshold value was not adjusted in accordance

with the amplitude of the oscillations that occur in the

baseline of the signal. It is important that this

adjustment is made taking into account the noise

present in the acquired signals.

Figure 5: Same EMG signal acquisition using a lower value

of DesactDelay parameter (250 instead of 350).

Finally, looking at (a) and (c), and comparing with

figure 1, it can be seen that, as expected, the amount

of onset/offset detections during the three muscle

activations has suffered an significant increase as the

two parameters, arraySize and activation, were

reduced.

EMG signal amplitude (a.u.)

Algorithm for Onset/Offset Detection of EMG Signals for Real-time Control of a Low-cost Open-source Bionic-hand

875

Figure 6: Same EMG signal acquisition using a lower value of: (a) and (b) arraySize parameter (150 instead of 250); (c) and

(d) also of activation parameter (0.1 instead of 0.2).

4 REAL-TIME APPLICATION

After testing the algorithm offline in Matlab, it was

necessary to find a platform capable of providing a

correct environment for its transition to the

application of the algorithm in real-time control of a

bionic hand, executed by a standalone Arduino Uno.

As shown in figure 7, previous tests were performed

with the accelerometer module of BITalino, due to its

greater ease in data analysis. These data were

acquired in parallel by Arduino and BITalino,

enabling the simultaneous visualization of the

acquired data, using the Serial Monitor and Serial

Plotter, in the first case, and Opensignals, in the

second case.

Through the correct use of the flags, already

presented in table 1, for the control of the algorithm's

flow, it was possible to obtain a satisfactory success

in the detection of the onset/offset of activations with

the accelerometer sensor.

Subsequently, the necessary adaptations were

made to the code for the acquisition of data from the

EMG sensor, namely in determining the values of the

three parameters described above. Furthermore,

although the sampling rate is adjustable, in these tests

a cycle time of one millisecond had been used. Both

in the selection of this value and in the arraySize

depth, there are constraints imposed by Arduino

Uno's limitations, which were met, but did not

prevent the successful application of this algorithm,

as shown in table 2, for the detection of muscle

activation when random gestures are performed for

the control of a bionic hand.

Figure 7: Schematic of previous tests setup. First with

accelerometer (A) and afterwards with EMG module (B).

0 2000 4000 6000 8000 10000 12000 14000

Time (ms)

350

400

450

500

550

600

650

700

750

[arraySize; DesactDelay; activation]=[150; 250; 0.2]

0 2000 4000 6000 8000 10000 12000 1400

350

400

450

500

550

600

650

700

750

[arraySize; DesactDelay; activation]=[150; 250; 0.2]

0 2000 4000 6000 8000 10000 12000 14000

Time (ms)

350

400

450

500

550

600

650

700

750

[arraySize; DesactDelay; activation]=[150; 250; 0.1]

0 2000 4000 6000 8000 10000 12000 14000

350

400

450

500

550

600

650

700

750

[arraySize; DesactDelay; activation]=[150; 250; 0.1]

Time

(

)

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Table 2: Results of success on detection of muscle

activation from random gestures.

Muscle activation rando

# performe

# detected 18

# not detected 0

# false activations 0

Success rate 100%

Error rate 0%

This evaluation of the success of this algorithm in

real-time muscle activation detection was carried out

using a servo-driven bionic one-hand controller

prototype, as shown in Figure 8.

Figure 8: Photo of the prototype of the bionic-hand

controller.

Additional factors were identified that may reduce

detection success such as the noise in the EMG

signals coming from electromagnetic interference,

namely that the different rotation of the servomotors

to carry out each person generates different noise.

Noise was also noted as a result of forearm movement

artifacts. Due care was taken with regard to noise

reduction by reducing the length of cables and

winding them up. Even with the conditions presented,

the results obtained for different gestures were

satisfactory, as shown in table 3.

5 DISCUSSION AND

CONCLUSIONS

Preliminary results from the application of this

algorithm in real-time detection of muscle activations

are promising. The method based on smoothing the

acquired signal from the EMG sensor, using flags in

order to identify for each acquisition the global state

of muscle activation, proved to be adequate for the

proposed objective. It is now necessary to broaden

and deepen this assessment in order to validate these

results. This will allow an optimization of the three

parameters defined in the algorithm, through a better

characterization of the factors with an impact on that

decision. In fact, the analysis performed did not

address the precision in determining the time interval

for muscle activation, which will be an important

factor in the evaluation of the proposed algorithm.

This will be a next objective, as this initial work

focused on demonstrating the applicability of this

algorithm, regarding the detection of muscle

activation in a real environment.

This work also aimed to evaluate the real-time

application of the algorithm, using a hardware

platform based on Arduino UNO, a cheap

microcontroller, as it is part of a project to develop a

very low-cost bionic hand. Thus, the level of

requirement raises, as the limitations in terms of

microcontroller performance, both in terms of

processing and memory, significantly reduce the

options in terms of algorithm. Even so, it was possible

to implement this algorithm with a sampling rate of 1

kHz, without an evaluation of the response time for

the control of the bionic hand, as this is not the

objective of this work.

The option for Micro Servo SG90 servomotors

also contributed to the low cost of the platform used.

However, it was possible to identify that they

constituted an additional source of noise, which

affected the amplitude of the fluctuations in the base

level of the signal, with the muscle relaxed,

depending on the gesture associated with the anterior

muscle activation. It is also worth remembering that,

contrary to what happens in the algorithms described

in the literature, for this real-time application of the

algorithm, the pre-processing with the use of filters

was not carried out, using the raw EMG signal

instead.

Table 3: Results of success on detection of muscle activation for the execution of different gestures.

Muscle activation Close Open Poin

Pinch

# performe

20202010

# detecteds 20 14 20 10

# not detected 0200

# false activations 1420

Success rate 100% 70% 100% 100%

Error rate 5% 30% 10% 0%

Algorithm for Onset/Offset Detection of EMG Signals for Real-time Control of a Low-cost Open-source Bionic-hand

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Thus, given all these constraints, it will be

difficult for any algorithm to be able to determine

with high precision the time interval of each muscle

activation. Despite this, future work will be focused

on optimizing the algorithm presented in this paper,

and subsequent integration into the bionic hand

control software, in order to characterize it in terms

of the success rate in performing the different

gestures and in its response time.

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De Luca, C.J. (1997). The Use of Surface

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Di Fabio, R.P. (1987). Reliability of computerized surface

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