Physical Burden in Manual Patient Handling:

Quantification of Lower Limb EMG Muscle Activation Patterns of

Healthy Individuals Lifting Different Loads Ergonomically

Anna Brinkmann

, Conrad Fifelski-von Böhlen

, Sandra Hellmers

, Ole Meyer

Rebecca Diekmann

and Andreas Hein

Assistive Systems and Medical Device Technology, Carl von Ossietzky University of Oldenburg,

26129 Oldenburg, Germany

Keywords: Manual Patient Handling, Nursing, Care, Health Monitoring, EMG, Muscle Activity, Biomechanics.

Abstract: Manual patient handling is a challenging part of daily care and leads to high mechanical loads as well as to

the development of degenerative diseases, e.g. lower back pain. To prevent musculoskeletal overload effects,

the use of ergonomic working techniques is essential as well as improving caregivers’ functional ability.

However, most of the studies do not consider these aspects and biomechanical evaluations including dynamic

electromyography (EMG) are rarely analyzed. In this work, we focus on the quantification of lower limb

EMG muscle activation patterns of healthy caregiver students in an experimental setup. The extent of lifting

different loads ergonomically is analyzed and similarities/dissimilarities of dynamic EMG data of three lower

limb muscles are investigated via cross-correlation calculation. One of the main findings of our investigation

is an indication of a more consistent mean activity of the quadriceps and hamstring musculature, as the load

to be lifted increases. Furthermore, we found an intra- as well as an interindividual similarity of EMG muscle

activation patterns regarding time and shape of the signals generated during all of the conducted lifting tasks

with a predominantly high cross-correlation coefficient for the selected muscles of the lower limb.

1 INTRODUCTION

Manual patient handling is one of the most significant

challenges in care and leads to high mechanical loads

as well as to the development of degenerative dis-

eases, e.g. lower back pain (Hwang et al., 2019; Choi

and Brings, 2016; Jäger et al., 2013). In particular,

lift, hold and handle especially overweight and obese

patients manually is physically demanding and leads

to a compressive strength of the lumbar spine of up to

9 kN (Choi and Brings, 2016; Jäger et al., 2013).

To prevent musculoskeletal overload effects sig-

nificantly, the correct use of technical devices as well

as ergonomic caregiving strategies like supervised er-

gonomic exercise training programs are essential

(Hwang et al., 2019; Choi and Brings, 2016; Weißert-

https://orcid.org/0000-0001-5228-4947

https://orcid.org/0000-0002-6118-2755

https://orcid.org/0000-0002-1686-6752

https://orcid.org/0000-0002-9964-5591

https://orcid.org/0000-0001-9793-3832

Horn et al., 2014; Jäger et al., 2013; Michaelis and

Hermann, 2010).

A functional approach to ergonomic working

strategies and the improvement of caregivers’ power

as well as functional ability is squat training (Kusma

et al., 2015; Jäger et al., 2013; Baum et al., 2012) as

the squat is biomechanically as well as neuromuscu-

lar similar to many activities of daily living, e.g.

standing up from a chair, demanding the musculo-

skeletal system of the human body more than 50 times

per day (Wang et al., 2019). As squatting positions

are also part of ergonomic manual caregiving rou-

tines, e.g. standing a patient up for transfer, the squat

is frequently used in exercise programs of strength

and conditioning as well as in physical therapy (Ya-

vuz and Erdag, 2017). In this case, lumbosacral loads

can be compensated by strengthening the lower limb

Brinkmann, A., Böhlen, C., Hellmers, S., Meyer, O., Diekmann, R. and Hein, A.

Physical Burden in Manual Patient Handling: Quantiﬁcation of Lower Limb EMG Muscle Activation Patterns of Healthy Individuals Lifting Different Loads Ergonomically.

DOI: 10.5220/0010247804510458

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 451-458

ISBN: 978-989-758-490-9

451

and back muscles. Thus, effective load transfer from

the lumbar spine to the pelvis is achieved and shear-

ing of the sacroiliac joints through compression is

prevented (Vleeming and Stoeckart, 2007; Richard-

son et al., 2002).

Common faults in squatting exercises of daily liv-

ing as well as in the context of professional caregiving

are faster rising hips than shoulders and thus resulting

in an increasing flexion of the trunk (Hellmers et al.,

2021; Yavuz and Erdag, 2017). In this case, the dis-

tance between hips and shoulders is diminished in

vertical direction when rising upright from squatting

and the lumbar load increases (Hellmers et al., 2021;

Yavuz and Erdag, 2017). The aim of squatting exer-

cises is to train quadriceps musculature around the

knee and hip joints, thereby strengthening the lower

back (Yavuz and Erdag, 2017). By using the squat as

an ergonomic working strategy for lifting patients,

quadriceps muscles are activated, resulting in a more

consistent mean activation of the back extensor mus-

cles (Brinkmann et al., 2020a; Brinkmann et al.,

2020b).

In recent literature, various scientific articles on

analyzing caregiving activities exist. These deal with

both the identification of psychological as well as

physical stress of healthcare workers and the

enhancement of existing strategies for preventing job-

related back disorders (Cheung et al., 2020; Vinstrup

et al., 2020; Hwang et al., 2019; Choi and Brings,

2016; Höhmann et al., 2016; Zhao et al., 2016; Kusma

et al., 2015; Weißert-Horn et al., 2014; Jäger et al.,

2013; Baum et al., 2012; Aiken et al., 2012; Michaelis

and Hermann, 2010). However, most of the studies do

not consider functional aspects in this context, such

as physical functionality of the caregivers. In

addition, the effects of applying ergonomic working

techniques and its biomechanical evaluation, in-

cluding dynamic electromyography (EMG) for

quantifying muscle activation patterns objectively,

are rarely analyzed (Cheung et al., 2020; Vinstrup et

al., 2020; Hwang et al., 2019).

In this work, we focus on the quantification of

lower limb muscle activity of healthy caregiver

students in an experimental setup. The extent of

lifting different loads ergonomically is analyzed and

similarities/dissimilarities of dynamic EMG data of

three lower limb muscles are investigated via cross-

correlation. We hypothesize a similar EMG activation

for all three conditions while mean muscle activity

increases with lifting heavier weights. The aim is the

assessment of potential amplitude independent

changes of EMG muscle activation patterns as a

function of different ergonomic lifting conditions.

2 MATERIALS & METHODS

2.1 Study Design

In the case study (ethical vote: Drs.EK/2019/004),

five healthy caregiver students (n = 5, 3 female and 2

male students aged between 21 to 45) conduct three

different dynamic lifting tasks:

(1) lifting the own body weight by rising from a

chair to an upright position (Figure 1a, (1)),

(2) lifting a patient simulator (13 kg) from the edge

of a motorized care bed to a standing position

(Figure 1a, (2)),

(3) lifting a patient-imitating subject (patient)

(female, 28 years, 63 kg) upright from the edge

of a motorized care bed (Figure 1a, (3)).

To avoid overloading the caregivers while lifting, a

physiotherapist supervises the tasks.

In the first task (Figure 1a, (1)), the caregivers’

initial position is a vertical trunk with crossed arms.

The feet are placed flat on the floor in shoulder width

and the knee angle is > 90° to avoid extreme joint load

(Slater and Hart, 2017). While rising to fully standing

upright, stability is maintained through muscle

activation. In this case, the components balance,

coordination and lower limb strength as well as power

are covered, which are important in view of analyzing

caregivers’ physical function quantitatively (Hardy et

al., 2010). All in all, each caregiver student repeats

the task five times. The second as well as the third

lifting task (Figure 1a, (2) and (3)) are conducted in

accordance to the Kinaesthetics (Hatch, 2003) care

conception. Therefore, the lifting is executed in

different stages and is foresighted ergonomic planned

with a consistent use of aids. The care bed is adjusted

to an appropriate working height, so that the patient’s

as well as the patient simulator’s feet are flat on the

floor while sitting at the bed’s edge. The caregiver

stands parallel to the patient and slightly squats

bending down to the patient for lifting up. The knee

angle is > 90°, as extreme joint load is thus prevented

(Slater and Hart, 2017). In the second task, the

caregiver puts his arms around the simulated patient

and lifts while rising from squatting (Figure 1a, (2)).

Then, both are in an upright position. Compared to

the second lifting task, there is an active interaction

between patient and caregiver in the third lifting task.

In this case the patient puts his arms around the

caregiver (Figure 1a, (3)), so that the functionality of

the patient can be used for cooperation while lifting.

Then, the caregiver also puts his arms around the

patient and shifts the own body weight while

remaining a straight back to finally lift up.

HEALTHINF 2021 - 14th International Conference on Health Informatics

452

Figure 1: Kinematic (a) and EMG data (b) of three different ergonomic lifting tasks of one exemplary study participant.

Kinematic data is shown in three-dimensional point clouds of the respective lifting task. The time courses for muscle activity

data of three muscles of the lower limb (vastus medialis (VM), rectus femoris (RF) and biceps femoris (BF)) are shown as a

function of the respective lifting task cycle for squatting and lifting for task (1) – (3).

2.2 Biomechanical Data Collection

The procedure for biomechanical data collection is

based on our existing Healthcare Prevention System

(Brinkmann et al., 2020a; Brinkmann et al., 2020b).

Thus, the kinematics of the moving body and its seg-

ments, the kinetics (external ground reaction forces)

and muscle activities of the caregivers’ lower limb are

recorded in order to quantify, assess and evaluate the

executed processes biomechanically.

By direct measurement techniques, a 3D multi-

depth image camera system (Fifelski et al., 2018)

record the data required for motion analysis and a

force plate is used for the measurement of occurring

external ground reaction forces while transferring the

patient. Non-invasive surface EMG records electrical

action potentials associated with muscle contraction

and is the main focus in this work. EMG is used in

order to gain information on the activation behavior

of the following selected muscle groups of the

caregivers’ thigh in task (1) – (3): vastus medialis

(VM), rectus femoris (RF) and biceps femoris (BF).

These muscles are part of the knee extensors as well

as the hip extensors and are thus primarily active

during the conducted dynamic squat exercises. The

electrodes are placed in accordance with SENIAM

guidelines (Hermens et al., 1999). For the acquisition

process, Dasy-Lab 4.010 software as well as an EMG

device from Biovision (Biovision Inputbox) and

bipolar surface electrodes (Ø 14 mm; 10 mm inter-

electrode distance) are used (GE Medical/Hellige).

By local amplifiers, an amplification of the signal

with 2500 Hz is done.

2.3 Data Analysis

In a first step, recorded dynamic EMG data is recti-

fied and then smoothed via Root Mean Square (RMS)

(Figure 2). Then, the data recorded while lifting the

own body weight is cut according to kinematic and

kinetic data representing the basis for analyzing the

different lifting tasks. In task (1), a lifting cycle starts

with sitting and ends with fully standing (Figure 1b,

(1) and Figure 2).

Physical Burden in Manual Patient Handling: Quantiﬁcation of Lower Limb EMG Muscle Activation Patterns of Healthy Individuals Lifting

Different Loads Ergonomically

453

Figure 2: Rectified and RMS smoothed EMG data of VM

activity in task (1) for one exemplary study participant.

Due to the fact, that every cycle reveals a slightly dif-

ferent duration for each participant as well as between

all participants, time normalization is applied using

linear interpolation function. Thus, a mean lifting

signal is calculated separately for each participant and

muscle. Then, an intraindividual as well as an in-

terindividual normalized cross-correlation analysis is

done by calculating cross-correlation coefficient (R-

value) at zero time lag to test dynamic EMG data for

similarities/dissimilarities (Geiger et al., 2019; Nel-

son-Wong et al., 2009; Wren et al, 2006) for each

muscle and each lifting task as follows:

𝑅



𝜏 

𝑁

∑

𝑥



𝑥𝑦

∙



𝑦





𝑁



∑

𝑥



𝑥







∑

𝑦



𝑦







(1)

with 𝑥



and 𝑦



as the two signals to be compared. 𝜏 is

the discrete temporal time shift, 𝑁 is the number of

data points in the respective signal and 𝑓



is the

original sample frequency (Nelson-Wong et al.,

2009).

Via cross-correlation the comparison of two signals

regarding timing and shape is possible, while

amplitude is not considered. Therefore, the signals

mean power is also reflected by using RMS, while its

mean value qualifies gross innervation input for

respective muscles. This step is then followed by

comparing mean EMG activity for each study

participant when lifting their own body weight in task

(1). In this case, we analyse the data due to similar

mean muscle activation patterns while lifting with an

interindividual point of view. The participants, who

show plausible mean muscle activation patterns in

task (1) while rising from a seated position to an

upright position and thereby lifting the own body

weight (Wang et al., 2019; Roldán-Jiménez et al.,

2015; Cuesta-Vargas and Gonzáles-Sanchez, 2013;

Roebroeck et al., 1994) are therefore constituted to

one functional group and considered for the

evaluation via cross-correlation calculation.

3 RESULTS

For concentric knee extension when lifting (Figure

1a), VM and RF contract at the same time while

hamstring muscle activation (BF) sustains the hip

(Figure 1b). All in all, three out of five study

participants show plausible mean muscle activation

patterns while rising from a seated position to an

upright position and thereby lifting the own body

weight in task (1) (Figure 1a, (1)). This means, that

the gross innervation input of the analyzed muscles is

highest for VM, followed by RF and BF (Wang et al.,

2019; Roldán-Jiménez et al., 2015; Cuesta-Vargas

and Gonzáles-Sanchez, 2013; Roebroeck et al.,

1994). Accordingly, these participants are constituted

to one functional group and therefore considered for

the evaluation (Figure 3). The other group of partici-

pants show different mean activation patterns with a

predominantly high activation level of RF and are not

considered for further analysis in this work.

For the functional group of study participants,

mean muscle activities of VM, RF and BF while lift-

ing the own body weight in task (1) (Figure 1a, (1))

are: VM = 350 mV, RF = 240 mV and BF = 80 mV

(Figure 3, (1)).

While lifting the patient simulator in task (2) (Fig-

ure 1a, (2)), mean muscle activities of VM, RF and

BF are: VM = 220 mV, RF = 190 mV and BF = 75

mV (Figure 3, (2)).

In task (3), while lifting the patient (Figure 1a,

(3)), mean muscle activities of VM, RF and BF are:

VM = 250 mV, RF = 245 mV and BF = 220 mV (Fig-

ure 3, (3)).



Figure 3: Mean muscle activity data of VM, RF and BF for

task (1) – (3).

Comparing the dynamic mean muscle activity data of

the conducted lifting tasks regarding the functional

group of study participants, similar mean muscle

activation patterns are present (Figure 3). Due to the

gross innervation input of the analyzed muscles while

lifting different loads ergonomically, the highest

value is found for VM, followed by RF and BF

(Figure 3). Comparing the tasks (1), (2) and (3), the

highest mean muscle activity values for VM and RF

HEALTHINF 2021 - 14th International Conference on Health Informatics

454

are found for lifting the own body weight from a

seated position (Figure 3, (1)), followed by lifting the

patient (63 kg) (Figure 3, (3)) and lifting the patient

simulator (13 kg) (Figure 3, (2)).

The delta between mean muscle activity of VM

and RF is 110 mV while standing up, 30 mV while

standing the patient simulator up (13 kg) and 15 mV

while standing the patient up (63 kg). Accordingly,

the delta between mean muscle activity of RF and BF

is 160 mV while standing up, 115 mV while standing

the patient simulator up and 25 mV while standing the

patient up. Furthermore, there is an increase of BF’s

mean muscle activity for all of the three lifting tasks

of up to 145 mV while standing the patient up com-

pared to lifting the own body weight as well as lifting

the patient simulator. In detail, comparing task (2)

and (3), the deviation of the delta of RF and BF is

78% while lifting the higher weight (63 kg) in com-

parison to lifting the patient simulator (13 kg) (Figure

3). In this case, the deviation of the delta of VM and

RF is 50 %.

Figure 4 shows the dynamic EMG data of the lift-

ing parts of task (1), (2) and (3) for one exemplary

study participant and the calculation of the R-values

is presented as a function of phase shift with an

intraindividual high similarity of EMG activation for

all three conditions.

All calculated intraindividual R-values for the

functional group of study participants are shown in

Table 1. Here, the R-values show high to very high

correlation for each muscle among the different lift-

ing tasks and for each study participant. However,

lower R-values and greater variability are found for

the BF within the execution of the different lifting

task of one participant (437, Table 1).

Interindividual comparison of the dynamic EMG

data (Table 2) show very high R-values for VM,

averaging > 0.90. In this case, the similarity for the

Table 1: Intraindividual R-values for each muscle and the

respective lifting task correlation.

ID Muscle

(1) ↔ (2) (1) ↔ (3) (2) ↔ (3)

424

VM 0.87 0.87 0.98

RF 0.80 0.82 0.99

BF 0.98 0.88 0.87

437

VM 0.89 0.86 0.95

RF 0.89 0.94 0.97

BF 0.68 0.80 0.67

471

VM 0.89 0.90 0.96

RF 0.99 0.99 0.99

BF 0.96 0.91 0.92

VM within the functional group of study participants

is R = 0.96 ± 0.005 for test (1), R = 0.96 ± 0.014 for

test (2) and R = 0.96 ± 0.005 for test (3) (Table 2).

The interindividual cross-correlation result for RF is

R = 0.91 ± 0.026 for test (1), R = 0.89 ± 0.069 for test

(2) and R = 0.97 ± 0.012 for test (3) (Table 2). By

comparing muscle activity data for BF within the

different study participants (Table 2), the R-value is

R = 0.95 ± 0.022 for test (1), R = 0.96 ± 0.025 for test

(2) and R = 0.86 ± 0.043 for test (3).

Figure 4: EMG data (a) and R-values (b) for task (1) – (3)

and for one exemplary study participant. EMG time courses

of VM, RF and BF are shown as a function of the respective

lifting task cycle. R-value calculation is presented as a func-

tion of phase shift.

Table 2: Interindividual R-values for each muscle and the

respective lifting task correlation.

Muscle Test

424 ↔ 437 424 ↔ 471 437 ↔ 471

(

)

0.95 0.96 0.96

(

)

0.94 0.97 0.97

(

)

0.96 0.96 0.97

(

)

0.93 0.87 0.92

(

)

0.83 0.99 0.86

(

)

0.96 0.99 0.97

(

)

0.93 0.98 0.94

(

)

0.99 0.95 0.93

(

)

0.92 0.82 0.84

Physical Burden in Manual Patient Handling: Quantiﬁcation of Lower Limb EMG Muscle Activation Patterns of Healthy Individuals Lifting

Different Loads Ergonomically

455

4 DISCUSSION

We focused on the quantification of lower limb EMG

muscle activation patterns of healthy caregiver

students while lifting different loads ergonomically.

In an experimental setup in the field, the extent of

kinematic, kinetic and muscular activity is investi-

gated while three different dynamic lifting tasks are

conducted (Figure 1).

In each task, the caregivers’ stability is maintained

through muscle activation while distributing the own

body weight evenly before standing up from a seated

position as well as lifting the simulated patient (13

kg)/the patient (63 kg). In accordance with literature

findings (Yavuz and Erdag, 2017; Aspe and Swinton,

2014; Paoli et al., 2009; Boyden et al., 2000; McCaw

and Melrose, 1999), mean muscle activity increases

with lifting higher loads in our experimental case

study. A more consistent mean activity of the quadri-

ceps and hamstring musculature is indicated, as the

load to be lifted gets higher. Thereby, concentric knee

extension and eccentric resistance to knee flexion

activates the quadriceps muscles (Figure 1b). The

hamstrings are quadriceps’ antagonists, as these mus-

cles oppose knee extensor moments (Yavuz and

Erdag, 2017). However, in squatting exercises RF and

BF paradoxically co-contract. With increasing load,

BF muscle activity increases as well (Figure 1b, (3)).

The effect of increasing mean muscle activity of BF

in our case study could be due to co-contraction for

stabilizing the knee as well as the pelvis while turning

from an eccentric to a concentric movement. In future

research, muscle fatigue could be another relevant

topic. Literature findings indicate an increasing

muscle fatigue of the knee extensors with increasing

task repetitions (Roldán-Jiménez et al., 2015). For

this purpose, the repetitions of task (1) should be in-

creased in future studies.

For the quantification of the EMG muscle

activation patterns generated in our experimental case

study, we use cross-correlation calculation for com-

paring the data from different lifting scenarios and

different individuals objectively. In a first step, the

muscle activation patterns of lifting the own body

weight in task (1) are intraindividual analyzed for the

functional group of study participants. Here, cross-

correlation results (R-values) show similar activation

for the five lifting cycles with slightly differences in

form as well as in duration. By using linear in-

terpolation function for intraindividual normalized

cross-correlation analysis at zero time lag, the R-

values indicate a high similarity between different

lifting patterns (Table 1) and a significant similarity

when comparing task (2) and (3) with somewhat

moderate correlation for the BF of one participant

(Table 1, 437, R = 0.72  0.060). This may reflect a

greater variability regarding muscle activation within

different lifting scenarios as well as a sensitivity of

this muscle due to its biarticular function (Wren et al.,

2006). In this case, a greater variability could be also

due to a knee angle < 90°, which has to be verified in

future studies. Furthermore, muscle’s length and

overlying fat mass could be the reason for an in-

creasing sensitivity to EMG electrode placement

(Wren et al., 2006). Although the BF as well as the

RF in some cases exhibit a greater variability than the

VM (Table 1), the R-values are still high. Comparing

the dynamic EMG data of the functional group of

study participants interindividual (Table 2), very high

R-values for VM, averaging > 0.90, are found for all

lifting scenarios. In future research, the constitution

of study participants to functional groups needs to be

further investigated. In this case, cross-correlation

analysis could be used to verify inter- as well as intra-

individual similarities/dissimilarities.

It should be noted, that no real patient was

recruited for our case study. Although, the use of the

patient simulator in task (2) prevents unintentional

subliminal cooperation and supportive behavior

throughout the tests, the variety of possible non-co-

operating patient behavior of e.g. anesthetized or

obese patients is not fully covered. This is due to the

low weight of the patient simulator (13 kg). However,

the weight of the patient (63 kg) is within a realistic

range and by using the patient’s functionality in task

(3), cooperative patient behavior is represented. Nev-

ertheless, it still has to be distinguished from lifting a

real patient. Therefore, it can be assumed, that muscle

activity data under realistic circumstances may be

higher than provided in this work.

The main findings of our experimental case study

are an intraindividual as well as an interindividual

similarity of EMG muscle activation patterns regard-

ing time and shape of the signals generated during all

of the three conducted lifting tasks. In this case, the

R-values are predominantly high for the selected mus-

cles of the lower limb, especially for the VM. These

results provide a first insight into the quantification of

EMG muscle activation patterns of healthy caregivers

lifting different loads ergonomically and serve as a

basis for further investigations with a larger study

population. Based on future research, the results may

enhance both supervised ergonomic exercise pro-

grams in the education of caregivers and to allow for

a more targeted use in training interventions from a

functional point of view.

HEALTHINF 2021 - 14th International Conference on Health Informatics

456

ACKNOWLEDGEMENTS

The authors would like to thank the Evangelische Al-

tenpflegeschule e.V. in Oldenburg (Evangelic Nurs-

ing School of Oldenburg) for participating the case

study.

REFERENCES

Aiken, L.H., Sermeus, W., Van den Heede, K., Sloane,

D.M., Busse, R., McKee, M., Bruyneel, L., Rafferty,

A.M., Griffiths, P., Moreno-Casbas, M.T., et al.

(2012). Patient safety, satisfaction, and quality of

hospital care: cross sectional surveys of nurses and

patients in 12 countries in Europe and the United

States. Bmj 2012, 344, doi: 10.1136/bmj.e1717

Aspe, R. R. and Swinton, P.A. (2014). Electromyo-

graphic and kinetic comparison of the back squat and

overhead squat, Journal of Strength and Condition-

ing Research, 28(10), pp. 2827-2836, doi:

10.1519/JSC.0000000000000462

Baum, F., Beck, B.B., Fischer, B., Gllüsing, R.,

Graupner, I. et al. (2012). Prävention von

Rückenbeschwerden; TOPAS_R–Konzept der BGW

für Pflege und Betreuung. Hamburg, Germany:

Berufsgenossenschaft für Gesundheitsdienst und

Wohlfahrtspflege.

Boyden, G., Kingman, J. and Dyson, R. (2000). A

comparison of quadriceps electromyographic

activity with the position of the foot during the

parallel squat, Journal of Strength and Conditioning

Research, 14(4), pp. 379-382, doi: 10.1519/

00124278-200011000-00002

Brinkmann, A., Fifelski, C., Lau, S., Kowalski, C.,

Meyer, O., Diekmann, R., Isken, M., Fudickar, S.

and Hein, A. (2020a). The AAL/Care Laboratory –

A Healthcare Prevention System for Caregivers.

Nanomaterials, 9(1), pp.1-10, doi:

10.1680/jnaen.19.00021

Brinkmann, A., Fifelski, C., Lau, S., Kowalski, C.,

Meyer, O., Diekmann, R. and Hein, A. (2020b).

Quantification of Lower Limb and Spine Muscle

Activity in Manual Patient Handling – A Case Study.

Proceedings of the 18th annual International

Conference on Informatics, Management, and

Technology in Healthcare (ICIMTH 2020), Athens,

Greece, 3-5 July 2020, pp. 249-252. doi:

10.3233/SHTI200541

Cheung, K., Dai, J., Cheung, C.L., Cho, H.K., Chow,

Y.L., Fung, K.Y., Lam, W.S., Li, H.L.C., Ng, S.Y.,

Ngan, M.J. and Szeto, G. (2020). The biomechanical

evaluation of patient transfer tasks by female nursing

students: With and without a transfer belt, Appl.

Ergon., 82. doi: 10.1016/j.apergo.2019.102940

Choi, S.D. and Brings, K. (2016). Work-related

musculoskeletal risks associated with nurses and

nursing assistants handling overweight and obese

patients: a literature review. Work, 53(2), pp. 439-

448, doi: 10.3233/WOR-152222

Cuesta-Vargas, A.I. and González-Sanchez, M. (2013).

Differences in Muscle Activation Patterns during Sit

to Stand Task among Subjects with and without

Intellectual Disability. BioMed Res Int. doi:

10.1155/2013/173148

Fifelski, C., Brinkmann, A., Ortmann, S.M., Isken, M.

and Hein, A. (2018). Multi Depth Camera System for

3D Data Recording for Training and Education of

Nurses. Proceedings of the 2018 International

Conference on Computational Science and

Computational Intelligence (CSCI). Las Vegas,

Nevada, USA, 12-14 December 2018, pp. 679-684,

doi: 10.1109/CSCI46756.2018.00137

Geiger, E.D., Behrendt, F., Schuster-Amft, C. (2019).

EMG Muscle Activation Pattern of Four Lower

Extremity Muscles during Stair Climbing, Motor

Imagery, and Robot-Assisted Stepping: A Cross-

Sectional Study in Healthy Individuals. BioMed

Research International 2019, doi:

10.1155/2019/9351689

Hardy, R., Cooper, R., Shah, I., Harridge, S., Guralnik,

J. and Kuh, D. (2010). Is chair rise performance a

useful measure of leg power? Aging Clin Exp Res,

22(5-6), pp. 412-8, doi: 10.1007/BF03324942

Hatch, M. (2003). Kinaesthetics. In: Health

Development and Human Activity. Urban and

Fischer, Munich, Germany, pp. 34-75.

Hellmers, S., Brinkmann, A., Fifelski- von Böhlen, C.,

Lau, S., Diekmann, R. and Hein, A. (2021).

Assessing Postures and Mechanical Loads during

Patient Transfers. 14

International Conference on

Health Informatics (HEALTHINF 2021).

Hermens, H.J., Freriks, B., Merletti, R., Stegeman, D.,

Blok, J. and Rau. G. (1999). European

Recommendations for Surface Electromyography:

Results of the SENIAM Project. Roessingh Research

Development, Enschede, the Netherlands. 8(2), pp.

13-54.

Höhmann, U., Lautenschläger, M. and Schwarz; L.

(2016). Belastungen im Pflegeberuf:

Bedingungsfaktoren, Folgen und Desiderate. In:

Pflege-Report 2016 – Die Pflegenden im Fokus.

Schwinger, A. (eds.). Schattauer, Stuttgart,

Germany, pp. 73-89.

Hwang, J., Kuppam, V.A., Chodraju, S.S.R., Chen, J.

and Kim, J.H. (2019). Commercially available

friction-reducing patient-transfer devices reduce

biomechanical stresses on caregivers’ upper

extremities and low back. Hum. Factors, 61(7), pp.

1125-1140. doi: 10.1177/0018720819827208

Jäger, M., Jordan, C., Theilmeier, A., Wortmann, N.,

Kuhn, S., Nienhaus, A. and Luttmann, A. (2013).

Lumbar-load analysis of manual patient-handling

activities for biomechanical overload prevention

among healthcare workers. Ann Occup Hyg., 57(4),

pp. 528-544. doi: 10.1093/annhyg/mes088

Jordan, C., Theilmeier, A. and Luttmann, A. (2010).

Biomechanische Bewertung der Belastung der

Physical Burden in Manual Patient Handling: Quantiﬁcation of Lower Limb EMG Muscle Activation Patterns of Healthy Individuals Lifting

Different Loads Ergonomically

457

Lendenwirbelsäule von Pflegepersonen beim

Bewegen von schwergewichtigen Patienten, Shaker,

Düren, Germany, 2010.

Kusma, B., Glaesener, J.-J., Brandenburg, S., Pietsch,

A., Fischer, K, Schmidt, J., Behl-Schön, S. and

Pohrt, U. (2015). Der Pflege das Kreuz stärken.

Germany: Berufsgenossenschaft für

Gesundheitsdienst und Wohlfahrtspflege. Trauma

und Berufskrankheit, 17(4), pp. 244-249.

McCaw, S.T. and Melrose, D.R. (1999). Stance width

and bar load effects on leg muscle activity during the

parallel squat, Medicine and Science in Sports and

Exercise, 31(3), pp. 428-436, doi: 10.1097/

00005768-199903000-00012

Michaelis, M. and Hermann, S. (2010). Evaluation des

Pflegekonzepts rückengerechter Patiententransfer in

der Kranken-und Altenpflege. Langzeit-Follow-Up

zur Ermittlung der Nachhaltigkeit präventiver

Effekte. In: Schriftenreihe der Bundesanstalt für

Arbeitsschutz und Arbeitsmedizin (F2196). Dort-

mund.

Nelson-Wong, E., Howarth, S., Winter, D.A., Callaghan,

J.P. (2009). Application of Autocorrelation and

Cross-Correlation Analyses in Human Movement

and Rehabilitation Research. Journal of Orthopaedic

& Sports Physical therapy, 39/4, pp.287 – 295.

Paoli, A., Marcolin, G. and Petrone, N. (2009). The ef-

fect of stance width on the electromyographical ac-

tivity of eight superficial thigh muscles during back

squat with different bar loads, Journal of Strength

and Conditioning Research, 23(1), pp. 246-250, doi:

10.1519/JSC.0b013e3181876811

Richardson, C.A., Snijders, C.J., Hides, J.A. et al.

(2002). The relationship between the transversus ab-

dominis muscle, sacroiliac joint mechanics and low

back pain. Spine J, 27(4), pp. 399-405, doi:

10.1097/00007632-200202150-00015

Roebroeck, M.E., Doorenbosch, C.A.M., Harlaar, J., Ja-

cobs, R. and Lankhorst, G.J. Biomechanics and mus-

cular activity during sit-to-stand transfer. Clin Bio-

mech 1994, 9, pp. 235-244. doi: 10.1016/0268-

0033(94)90004-3

Roldán-Jiménez, C., Bennett, P. and Cuesta-Vargas, A.I.

(2015). Muscular Activity and Fatigue in Lower-

Limb and Trunk Muscles during Different Sit-To-

Stand Tests. PLOS ONE, 10(10), doi: 10.1371/jour-

nal.pone.0141675

Slater, L.V. and Hart, J.M. (2017). Muscle activation

patterns during different squat techniques. Journal of

strength and conditioning research, 31(3), pp. 667–

676.

Vinstrup, J., Jakobsen, M.D., Madeleine, P. and Ander-

sen, L.L. (2020). Biomechanical Load during Patient

Transfer with Assistive Devices: Cross-sectional

Study, Ergonomics, doi: 10.1080/ 00140139.2020.

1764113

Vleeming, A. and Stoeckart, R. (2007). The role of the

pelvic girdle in coupling the spine and the legs: a

clinical-anatomical perspective on pelvic stability.

In: Movement, Stability & Lumbopelvic Pain, 2nd

ed.; Vleeming, A.; Mooney, V.; Stoeckart, R.; Eds.;

Churchill Livingstone, Edinburgh, UK, pp. 113-137.

Wang, J., Siddicky, S.F., Oliver, T.E., Dohm, M.P.,

Barnes, C.L. and Mannen, E.M. (2019). Biomechan-

ical Changes Following Knee Arthroplasty During

Sit-To-Stand Transfers: Systematic Review, J Ar-

throplasty, 34(10), pp. 2494-2501, doi:

10.1016/j.arth.2019.05.028

Weißert-Horn, M., Meyer, M., Jacobs, M., Stern, H.,

Raske, H.-W. and Landau, K. (2014). „Ergonomisch

richtige‟ Arbeitsweise beim Transfer von

Schwerstpflegebedürftigen. Zeitschrift für

Arbeitswissenschaft, 68(3), pp. 175-184.

Wren, T.A.L., Do, K.P., Rethlefsen, S.A., Healy, B.

(2006). Cross-correlation as a method for comparing

dynamic electromyography signals during gait. Jour-

nal of Biomechanics 2006, 39, pp. 2714 – 2718. doi:

10.1016/j.biomech.2005.09.006

Yavuz, H.U. and Erdag, D. (2017). Kinematic and Elec-

tromyographic Activity Changes during Back Squat

with Submaximal and Maximal Loading. Applied Bi-

onics and Biomechanics, doi: 10.1155/2017/90

84725

Zhao, W., Lun, R., Gordon, C., Fofana, A.B., Espy,

D.D., Reinthal, M.A., Ekelman, B., Goodman, G.,

Niederriter, J. Luo, C. and Luo, X. (2016). A pri-

vacy-aware Kinect-based system for healthcare pro-

fessionals. Proceedings of the 2016 IEEE Interna-

tional Conference on Electro Information Technol-

ogy (EIT). Grand Forks, ND, USA, 19-21 May 2016,

pp. 205-210, doi: 10.1109/EIT.2016.7535241.

HEALTHINF 2021 - 14th International Conference on Health Informatics

458