Neuromotor Pattern of the Upper Limb in Hygiene Activities Using

Electromyography and Accelerometery Technology

Patrícia Santos

1,2,3 a

, Inês Garcia

, Carla Quintão

1,2 c

and Claúdia Quaresma

1,2 d

LIBPhys, NOVA School of Science and Technology, NOVA University of Lisbon,

2829-516 Caparica, Portugal

Physics Department, NOVA School of Science and Technology, NOVA University of Lisbon,

2829-516 Caparica, Portugal

Health Department, Superior School of Health, Polytechnic Institute of Beja, 7800-111 Beja, Portugal

Keywords: Upper Limb, Activities of Daily Living, Electromyography, Accelerometery, Technology, Biomechanics.

Abstract: The technology is a valuable resource for movement analysis, especially for complex movement patterns such

as those of the upper limb during activities of daily living (ADLs). Characterizing these patterns in healthy

individuals is crucial to detect abnormal and compensatory movements resulting from neurological

dysfunctions. This study aimed to characterize the neuromuscular activation pattern of the upper limb during

the washing of the contralateral limb in 36 healthy individuals. The Biosignalsplux® equipment was used to

monitor the activity of the main shoulder muscles, that is, Pectoralis Major (PM), Anterior Deltoid (AD),

Middle Deltoid (MD), Posterior Deltoid (PD), Upper Trapezius (UT) and Lower Trapezius (LT), through

electromyography (EMG) and accelerometry (ACC). The results show variations in the contraction pattern in

the different phases of the activity. With this study it was possible to establish the normalized pattern of the

activity of EMG and ACC of the shoulder complex and respective movement phases.

1 INTRODUCTION

Technology is crucial in the analysis of movement

patterns, especially when compared to more common

assessment tools based on scales that do not provide

a detailed and accurate analysis of motion (Alt

Murphy, 2006; Gil-Agudo et al., 2013; de los Reyes-

Guzmán et al., 2010). The use of sensors in

movement analysis has increased (Bleser et al., 2015;

Özdemir & Barshan, 2014), allowing the acquisition

of high-precision data (Jalloul et al., 2018),

particularly in the analysis of complex movement

patterns, such as those performed during Activities of

Daily Living (ADLs). Using biosensors, it is possible

to identify normal movement patterns in ADLs.

The relationship between the variables obtained

with the biosensors helps to understand in which

phases of activity a movement pattern appears to be

normal or not, helping to identify abnormal patterns

https://orcid.org/0000-0002-3569-2495

https://orcid.org/0009-0002-5357-5919

https://orcid.org/0000-0003-1015-4655

https://orcid.org/0000-0001-9978-261X

and, thus, detecting the presence of associated

dysfunctions early (Jalloul et al., 2018).

Impairment of the normal pattern of upper limb

movement is one of the most common neurological

sequelae (Nakayama et al., 1994), which leads to loss

of autonomy due to functional changes in the elbow

and shoulder, and consequently they also affect

normal reaching and grasping (Klein et al., 2011).

These motor compensations can result in

musculoskeletal pain or overuse injuries (Levin et al,

2009), accentuating existing disability.

Given that the involvement of the upper limbs is a

prerequisite for performing ADLs, it is essential to

objectively analyse the neuromotor pattern of the

upper limb in them (Gulde & Hermsdorfer, 2017),

however, there is a gap in studies at this level.

ADLs such as hygiene involve multiple tasks and

phases and need to be analysed from the perspective

of movement variability and contraction patterns of

Santos, P., Garcia, I., Quintão, C. and Quaresma, C.

Neuromotor Pattern of the Upper Limb in Hygiene Activities Using Electromyography and Accelerometery Technology.

DOI: 10.5220/0012463900003657

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 1, pages 799-806

ISBN: 978-989-758-688-0; ISSN: 2184-4305

799

muscle groups most likely to develop compensatory

patterns (Gulde & Hermsdorfer, 2017; Santos, P. et

al., 2022a). Prevention of compensatory movement is

one of the best practices, which includes early

detection and retraining of appropriate movements.

For prescribe restorative interventions, it is essential

to understand movement patterns and underlying

motor strategies (Valevicius et al., 2019).

1.1 Analysis of the Upper Limb

Movement Pattern Based on the

Phases of ADLs

Dividing complex motor tasks in different phases to

better analyse and characterize the movements, began

to be utilized in gait analysis studies. These studies

aimed to characterize the gait cycle pattern in

diversified groups through the analysis of kinematic

and biosignal parameters (Buddhadev et al., 2020).

In this analysis, a full gait cycle is normalized to

100%, with each phase of the cycle given a

corresponding percentage. The variables of each

phase are then analysed (Ewins & Collins, 2014).

Subsequently, the movement pattern of the upper

limb was also subjected to this type of analysis.

In studies of upper limb movement patterns,

various phases of ADLs were analysed. However, the

majority analysed, for example, the activity of

drinking from a cup, by variables such as the range of

motion in upper limb joints (Molina Rueda, 2012;

Santos, G. et al., 2018; Stanfield et al., 2018), the

duration of each phase (Stanfield et al., 2018), and the

execution speed (Alt Murphy et al., 2018).

Although less common, some studies have

examined the amplitude of muscle activation through

electromyography (EMG) in various phases of the

drinking activity (Molina Rueda et al., 2012, Santos,

P. et al., 2022a, 2022b), filling a glass (Ricci et al.,

2015), washing the contralateral limb, brushing hair,

eating soup, and brushing teeth (Santos, P. et al.,

2022a, 2022b). While some studies analyse phases of

ADLs, few characterize the pattern of muscle

activation through EMG and ACC, with

normalization of the movement cycle.

1.2 Characterization of the Movement

Pattern in ADLs Using EMG and

ACC

There is a gap in the characterization of the muscle

activation pattern in healthy subjects. Without this

information, it is not possible to identify

compensatory movements in individuals with

neurological pathology. Few studies have focused on

healthy subjects and the activation pattern of shoulder

muscles: Pectoralis Major (PM), Anterior Deltoid

(AD), Middle Deltoid (MD), Posterior Deltoid (PD),

Upper Trapezius (UT), and Lower Trapezius (LT).

These muscles are responsible for flexion (F),

extension (E), abduction (ABD), adduction (AD),

scapular elevation (SE), and scapular depression

(SD), respectively (Esperança Pina, 2017).

Firstly, Molina Rueda et al. (2012), found that in

the five phases of the activity of drinking from a cup,

the UT was activated in phases 3 (raising the cup to

the lips) and 4 (returning the cup to the table); the AD

in phase 1 (start the capture by moving the hand to the

cup) and phase 2 (grab the cup); the MD in phase 3

(raising the glass to the lips) and 4 (returning the glass

to the table); and the PD in phase 5 (release the cup

and the hand returns to the original position). Thus,

the UT, AD, and MD are activated in the first three

phases, corresponding to the range of movement from

the initial position until the cup reaches the mouth. In

the opposite movement, i.e., phases 4 and 5, there is

activation of the UT and DP.

Ricci et al. (2015) observed that during the

performance of ADL, specifically pouring water from

a jug into a glass, there is distinct activation of the

MD in the reaching phase, UT and MD in the

transport phase, and AD and MD in the release phase.

It is noteworthy that this activity deviates from others

due to the absence of a movement directed to the face.

In our team's recent investigations (Santos, P. et

al., 2022b), we identified that the muscles exhibiting

the highest activation during the contraction phase

(muscle activation increase) in the context of washing

the contralateral arm are the AD, UT, and PM.

Furthermore, Santos, P. et al. (2022a) conducted a

comprehensive analysis of the expected movements

and corresponding amplitude of activation during the

activity of washing the contralateral arm. The phases

include grasping (involving ADD), transporting to

the contralateral side (involving ADD, F, and SE),

and reaching the contralateral side (involving ADD,

F, and SE) as outlined in Table 1. These findings were

validated through EMG, where ADD was executed in

accordance with the anticipated patterns.

Regarding studies that analyse the linear

acceleration of the shoulder (in the x, y, z axes) and

relate these data to the phases of the activity, no study

has been found. Most studies use Inertia Movement

Units (IMU) or optoelectronic motion capture

systems to analyse the joint range of motion.

The aim of this study is to analyse and explore the

characteristics of the movement pattern during the

washing of the contralateral arm, specifically the

electrical activation amplitude the main shoulder

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

800

muscles, as well as the linear acceleration of the arm

in healthy individuals.

This activity is among the most disabling ADLs,

a consequence of the neurological pathology

sequelae, specifically hemiparesis. In these instances,

individuals face challenges in adequately performing

hygiene on the unaffected limb. Therefore, delving

into the study of this ADL holds particular

significance within a clinical context.

2 MATERIAL AND METHODS

This study was approved by the Ethics Council of

NOVA School of Science and Technology, located in

Almada, Portugal, and it was executed in the Physics

Department. Volunteers signed an informed consent.

2.1 Study Participants

The protocol was applied to 39 individuals, but 3

were excluded due to Bluetooth connection issues

between the Biosignalsplux® and the computer,

leading to failures in signal acquisition. The sample

was composed by 36 individuals (19 females and 16

males; 33 right-handed and 3 left-handed; age 28.8 ±

12.5 years; height 168.8 ± 7.1 cm). Exclusion criteria

included diagnosis of neuromotor, musculoskeletal,

cognitive or language injuries and changes in visual

acuity not compensated by glasses or contact lenses.

To each subject was attributed a code number, to

guarantee anonymity (Alt Murphy et al., 2006).

2.2 Experimental Setup and Protocol

The movement phases were stablished through two

cameras, placed at one meter away from the subject,

one in the coronal and other in the sagittal anatomical

plan, with a resolution of 30 frames per second.

EMG and ACC data was acquired through the

Biosignalsplux®. This equipment was wirelessly

connected to OpenSignals (r)evolution Software®

version 2.2.1, software used to obtain and display the

respective signals, and is specific to PLUX’s

biological signal collection hardware platforms (PluX

Wireless Biosignals, 2017). The acquisition

frequency is 1000 Hz. This set includes 8 sensor

inputs, 6 EMG electrode cables, an ACC with sensor

in the 3 coordinate axes and its tape. Due to

equipment limitations, it was decided to use only 2

axes, as the equipment has 8 cable connections, and 6

of them were being used in EMG.

The EMG sensors were placed using the bipolar

method, defined by SENIAM, from the center of each

electrode, in the belly of the same muscle and parallel

to the fibers. The reference electrode was placed on

the olecranon bone (zone of low electrical activity)

(Freivalds, 2011; Stegeman & Hermens, 2007).

In summary, the procedures for carrying out this

study were described in Santos, P. (2022b). The

subjects began the activity with the feet on the floor,

knees and hips flexed at 90º, upper limbs are

supported on the thighs, shoulders in a neutral

position, elbows flexed at 45º, forearms and hands

rest on the thighs. Before data collection was given

the opportunity to perform the movement, so that

subjects feel comfortable with the execution of it.

Participants were instructed to perform 5 trials. The

activity phases of the ADL are resumed in Table 1.

Table 1: Activity phases of washing the contralateral limb.

Activity phases of washing the contralateral limb

1. Grasping

2. Transporting to

the contralateral side

3. Reaching the

contralateral side

4. Return to the thigh 5. Return to initial position

2.3 Data Analysis

2.3.1 Movement Phases

Matlab® was used to analyse .avi videos files frame

by frame, with a function to register the frame where

each phase initialized and ended in an Excel file

(Table 1). The time (s) was retrieved from the frames

and camera frequency and calculated the mean and

standard deviation (SDT) of the time of each phase.

2.3.2 Electromyography

To obtain the EMG data in milli-Volt (mV), the

transfer function was retrieved from the BioPluX

website (PluX Wireless Biosignals, 2017). To reach

the zero offset, the mean was subtracted from the

signal and the absolute value was taken. The moving

average was applied to smooth out the signal waves,

considering that there should be a commitment to

softening the signal and removing important

information (Stegeman & Hermens, 2007).

Neuromotor Pattern of the Upper Limb in Hygiene Activities Using Electromyography and Accelerometery Technology

801

2.3.3 Accelerometer

The same procedures were applied to ACC signals:

subtraction of mean and move average filter. The

ACC needed to be calibrated. That procedure was

effectuated in the beginning of the study, and the

transfer function was applied to the raw data (PluX

Wireless Biosignals, 2019). The ACC was placed in

the lateral region of the arm, vertically aligned with

the lateral epicondyle (Curti et al., 2008). The x-

vector corresponds to the coronal anatomical plane

and y-vector to the sagittal.

2.3.4 Normative Pattern of Movement

The normative pattern for EMG and ACC describes

the mean behavior of the all sample used in this study,

and respective correlation with the ADL phases.

The ADL phases were determined by analyzing

video frames from the frontal plane. For each subject's

5 activity cycles, frame numbers marking the start and

end of each phase were recorded. Mean and SDT

values for these frame numbers were calculated and

converted to seconds based on the camera's acquisition

frequency (30 frames/s). This frame analysis informed

the definition of phase beginnings, ends, and durations,

as well as the segmentation of the signal into 5 cycles

and corresponding intervals. Since the goal was to

obtain the normative pattern of EMG contraction in the

present sample, the average of the signals was retrieved

after resampling time and amplitude, so the dimensions

of time and amplitude were equated.

Given that the duration of execution varies among

subjects, and that would be not possible to do the

mean, it was necessary to apply the MATLAB signal

processing function resample to the signal. This was

done to adjust the time axis in all signals to 8000 ms,

as this represented the maximum duration of the ADL

to avoid undersampling.

For graphical visualization, it was established that

8 seconds represent 100% of the activity duration. In

the signal, it was observed that after the subject

placed their hand on their thigh, marking the end of

the activity in the video, the muscle did not

immediately relax. It maintained some level of

contraction beyond 100% until fully relaxing. Hence,

the decision to include the interval between cycles,

which was resampled to 2000 ms, corresponding to

25% of the total time. For Figures from 1 to 9, the

following Matlab code was used:

figure;

function[lineOut,fillOut]=

stdshade(amatrix,0.1,[0,0,0],1:size(amatrix,

2),5);

stdshade(amatrix,alpha,acolor,F,smth);

The function draws the mean EMG signal with the

respective STD (Mussal, S., 2023). Variables of code:

amatrix (matrix with all values); alpha (transparency

of the line from 0 to 1); acolor (color of the SDT

shade; F (x axis steps); smth (smoothing factor).

3 RESULTS

3.1 Phases

As observed in Table 2, on average, during the ADL,

phase 4 is the task that takes the longest to be

performed (56.2

9.1), followed by phase 2 (18.4

4.6), phase 5 (10.7

8.0), phase 3 (10.4

3.8) and

phase 1 (4.4

2.3). The estimate mean time, in

absolute value, can be consulted in Table 2.

Table 2: Mean time interval of each movement phase (%)

and phase duration (%) and respective SDT.

Phases

Interval of

mean time (%)

Mean duration

± SDT (%)

Mean duration

± SDT (s)

[0, 4.4]

4.4 ± 2.3

0.2 ±0.1

]4.4, 22.7]

18.4 ± 4.6

0.7 ±0.2

]22.7, 33.1]

10.4 ± 3.8

0.4 ±0.2

]33.1, 89.3]

56.2 ± 9.1

2.1 ±0.5

]89.3, 100.0]

10.7 ± 8.0

0.4 ±0.3

3.2 Electromyography

The figures from 1 to 6 represent the mean EMG and

SDT, for all six muscles, during the 5 phases of the

ADL. It is observed in the figures that the amplitude

is increasing during phases 1 to 3, and the maximum

amplitude is reached close to the beginning of the

movement of phase 4 (33.1 time (%)), returning to the

thigh, where we detect the first inflexion point.

Muscles reach their maximum peak at different times,

but with a difference not greater than 7.0%. In

ascending order, the MD reaches the maximum value

at 34.6%; AD 34.8%; PD 38.1%; PM takes 41.5%;

UT and LT 41.5% (Table 3).

During phase 4, represented in yellow, for all

muscles except the PM and AD, a second inflection

point is observed, which counteracts the trend of

decreasing amplitude. In the MD and PD muscles this

inflexion point occurs in the middle of phase 4; in the

UT and LT at the end of phase 4, being most

noticeable in the UT. The third inflection point, which

determines the decrease in amplitude until the end of

activity, occurs for the MD, PD and LT muscles in the

the end of phase 4 and on the UT at the beginning

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802

Figure 1: Mean EMG and SDT of the muscle PM and

respective movement phases.

Figure 2: Mean EMG and SDT of the muscle AD and

respective movement phases.

Figure 3: Mean EMG and SDT of the muscle MD and

respective movement phases.

Figure 4: Mean EMG and SDT of the muscle PD and

respective movement phases.

of phase 5. The mean amplitude of maximum peaks,

in absolute values (mV), report the three muscles with

higher amplitude contractions are AD, PM and UT.

These values can be consulted in Table 3. In

summary, the PM and AD muscles have, on average,

one inflection point during activity, and the remaining

four muscles three inflection points. The rest phase,

represented in gray, gives information of the

relaxation of the muscle, proving that minimum

amplitude occurs after the end of phase 5, when

subjects return to the initial position.

Figure 5: Mean EMG and SDT of the muscle UT and

respective movement phases.

Figure 6: Mean EMG and SDT of the muscle LT and

respective movement phases.

Table 3: Mean time (%) of maximum normalized peaks by

muscle.

Muscle Mean time of

amplitude peak

(%)

Mean amplitude

contraction peak

SDT (mV)

PM 41.5 0.07

0.06

AD 34.8 0.09

0.06

MD 34.6 0.04

0.02

PD 38.1 0.02

0.02

UT 41.5 0.06

0.04

LT 41.5 0.02

0.02

3.3 Accelerometer

The figures 7 to 9 represent the mean ACC and SDT,

for x, y and sum of x and y vectors, during the 5

phases of the ADL. It is possible to observe that the

behavior of the ACC, with the normalized g-unit

amplitude from 0 to 1, is identical for the two

anatomical planes represented in the linear

acceleration. The sum of the two linear acceleration

vectors is given by figure 9. The minimum

acceleration occurs at the beginning of phase 1 and at

rest. The maximum acceleration occurs in phase 4 at

53.0% for vectors x and y, and at 46.7% for the sum

vector (Table 4).

Neuromotor Pattern of the Upper Limb in Hygiene Activities Using Electromyography and Accelerometery Technology

803

Figure 7: Coronal anatomical plan (vector x) linear

acceleration.

Figure 8: Sagittal anatomical plan (vector y) linear

acceleration.

Figure 9: Sum of the vectors x and y linear acceleration.

Table 4: Mean time (%) of maximum normalized peaks by

linear acceleration vector.

Vector

Mean time of

amplitude peak (%)

X 53.0

Y 53.0



𝑋



𝑌



46.7

4 DISCUSSION OF RESULTS

In EMG data results, two distinct phases (phase of

increased activation amplitude and a phase of

decreased amplitude) were observed in all muscle

groups. This pattern aligns with Molina Rueda et al.

(2012), Ricci et al. (2015), and Santos, P. et al.

(2022a, 2022b). While the studies by Molina Rueda

et al. (2012) and Ricci et al. (2015) focused on other

ADLs, these phases were a transversal characteristic.

The initial phase (increased amplitude)

corresponds to an isotonic concentric contraction.

There is a shortening of sarcomeres in the muscle

belly, leading to greater activation of muscle fibres

and includes the three initial phases (grasp, carry to the

contralateral side, and reach the contralateral side) and

the initiation of the phase 4 (return to the thigh) in all

muscles. This is supported by Table 2 and 3, where the

average peak amplitudes are in the percentage of mean

time amplitude peak corresponding to the beginning

of phase 4. These results align with Santos, P. et al.

(2022a), where, during washing the contralateral arm,

the three primary phases (grasp, transport to the

contralateral side, reaching the contralateral side)

corresponded to the phase of increased muscle

activation amplitude.

However, in the present study, by establishing the

beginning and end of each phase, demarcated in

Figures 1 to 6, we can analyse and confirm with more

precision that the phase of muscle activation of

increasing amplitude is associated at the beginning of

phase 4, which was not possible in previous studies.

Based on the results of the peak amplitude of

contraction in each muscle, we can see that the

muscles AD, PM, UT (Table 3) exhibits the highest

activation in motion from the thigh to the shoulder.

This is evident as these peaks occur until the beginning

of phase 4, which supports the motion

Figure 9: Sum of

the vectors x and y linear acceleration

analysis in Santos,

P. et al. (2022a), where the first movements are F,

ADD and SE, in which the AD, PM and UT muscles

are agonists (Esperança Pina, 2017).

Subsequently, the decrease in the amplitude, in

which there is an eccentric isotonic contraction, that

is, despite muscle activation, the amplitude decreases

as there is a progressive stretching of the sarcomeres.

According to Tables 2 and 3, this phase begins in

phase 4 (immediately after the maximum peaks of

activation of each muscle), continues until the final

phase (return to the initial position) and the rest phase.

However, in this decreasing phase, it is observed

(Figures 1 to 6) there are inflection points (MD, PD,

UT, and LT), with the most pronounced ones in MD,

PD and UT muscles between the second half of the

phase 4 and the beginning of phase 5. These results

supported by Santos, P. et al. (2022a), the inflection

peaks are aligned with those observed in the previous

study. According to the results, we can also infer that

this inflection peak in MD and PD occurs between the

last third of phase 4, that is, the phase in which contact

with the distal part of the contralateral limb is lost and

returns to the thigh requiring the activation of PD (E)

and MD (ABD) as seeing in Santos, P. et al. (2022a).

The inflection peak in UT can be justified by

motion analysis in the beginning of phase 5. To bring

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

804

the limb back to a position over the thigh, there must

be scapular elevation. The present study adds to the

comprehensive motion analysis of this activity in

phase 5 from our previous study P. et al. (2022a) by

incorporating the movement of SE.

Regarding the results with ACC, there is a peak, a

turning point, around the first half of phase 4. The

acceleration values increase in phases 1 and 2, with

increase observed in phase 3, reaching its maximum

peak at the beginning of phase 4. After this, a regular

decrease is noted until the end. This pattern is

observed both in the displacement of the arm along

the medio-lateral axis (x), the displacement of the arm

segment along the longitudinal axis (y), and in the

resultant vector of the sum of both.

Although in previous studies with EMG and ACC

in ADLs, age and sex were not considered into result

analysis, we believe these variables could impact the

outcomes. Nakatake, et al. (2023) noted that, in

drinking ADL, older individuals exhibited reduced

shoulder ABD amplitude, and females completed the

task more quickly. This prompts consideration for

exploring the influence of age and sex on muscle

activation amplitude patterns in future studies.

This study thus establishes a greater level of

precision in the analysis of the movement pattern in

the different phases of washing the contralateral limb.

5 CONCLUSIONS

This study unveils the normative movement pattern

of an ADL renowned for its challenge among

individuals with neuromotor diseases. The delineated

pattern, consisting of two distinct phases, culminates

in valuable insights into muscle activation dynamics.

Notably, the PM, AD, and UT emerge as key players

in the intricate sequence from thigh to shoulder, with

inflection points observed in the diminishing

amplitude phase, involving the MD, PD, and UT.

Conclusions drawn highlight the necessity for

future studies to validate and extend these results

across a wider age spectrum, particularly in contexts

where neurological conditions, like stroke, prevail.

Recommending the integration of EMG with gold

standard technologies, such as optoelectronic motion

capture systems, further emphasizes the commitment

to methodological precision. Despite equipment

constraints, these findings offer nuanced insights with

profound implications for clinical practice.

The comprehensive understanding of muscle

activation sequences, inflection points, and phase-

related nuances presented in this study has the

potential to revolutionize clinical interventions.

Tailoring rehabilitation programs to target specific

muscle groups can optimize motor function recovery,

profoundly impacting the quality of life and

independence of individuals. The acknowledgment of

limitations informs future research methodologies,

emphasizing a dedication to advancing clinical

assessment and treatment strategies.

ACKNOWLEDGEMENTS

This work was supported by national funds from FCT

– Foundation for Science and Technology, I.P.

through the UIDB/FIS/04559/2020 (LIBPhys-UNL).

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