Assessing Postures and Mechanical Loads during Patient Transfers

Sandra Hellmers

1 a

, Anna Brinkmann

1 b

, Conrad Fifelski-Von B

ohlen

, Sandra Lau

Rebecca Diekmann

and Andreas Hein

Assistance Systems and Medical Device Technology, Carl von Ossietzky University Oldenburg, Oldenburg, Germany

Geriatric Medicine, Carl von Ossietzky University Oldenburg, Oldenburg, Germany

Keywords:

Motion Capture, IMU, EMG, Care, Nursing, Musculoskeletal, Technology, Wearable, Posture, Kinect.

Abstract:

Socio-Demographic developments in industrialized countries cause a discrepancy between potential recipients

and providers of care. Caregivers experience high musculoskeletal loads during their daily work, which leads

to back complaints and a high rate of absenteeism at work. Ergonomically correct working can signiﬁcantly

reduce musculoskeletal load. In a study with 13 caregiver students, we analyzed body postures, muscle activ-

ities, and loads during the transfer of a patient from bed to wheelchair. Our measurement system consists of a

full-body motion capture system and a Multi Kinect System. Additionally, muscle activities were measured via

surface electromyography. According to recommendations for ergonomic working in the care sector, a system

was developed that recognizes potentially harmful postures based on the motion capture data. A result report

visualizes the skeleton model together with color-coded information about inclination and torsion angles. The

motion capture data was also related to EMG data and analyzed according to biomechanical assumptions.

1 INTRODUCTION

Socio-demographic developments of industrialized

countries are characterized by low birth rates and an

increasing life expectancy. Therefore, the number of

people reaching old age increases. The discrepancy

between the supply of caregivers and the demand for

caregivers will continuously grow. In an international

comparison, Germany already has the worst patient-

to-caregiver ratio in Europe, with measurable effects

on patient mortality rates and the stress experience of

caregivers (H

ohmann et al., 2016; Aiken et al., 2012).

Manual patient handling is one of the physiological

risk factors and leads to high mechanical loads in the

lower back of caregivers (Kliner et al., 2017; Hwang

et al., 2019; Choi and Brings, 2016; J

ager et al.,

2013). Particularly non-ergonomic movements and

postures lead to health problems. This results from

various strenuous activities such as deep bending or

twisting during manual patient transfer, e.g. transfer-

ring a patient from bed to wheelchair (Hwang et al.,

2019; Choi and Brings, 2016; J

ager et al., 2013). But

also working in harmful postures leads to back com-

plaints. Caregivers spend a total of two hours per

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

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

shift in a bent posture or bend down 1500 times per

shift (Weißert-Horn et al., 2014). High musculoskele-

tal strains and related spinal complaints are one of

the main reasons for the high rate of absenteeism at

work as well as for leaving the profession in profes-

sional nursing (J

ager et al., 2013; Weißert-Horn et al.,

2014). It is therefore important to relieve and sup-

port caregivers. Musculoskeletal stress can be signif-

icantly reduced by an ergonomically correct method

of caregiving (Hwang et al., 2019; Choi and Brings,

2016). Also, prevention programs including various

ergonomic measures can improve the well-being of

the back and reduce the physical strain on caregivers

(Michaelis and Hermann, 2010).

To prevent back complaints, it is therefore important

to train caregivers to work in a back-friendly way and

to avoid harmful postures and actions. Therefore, we

developed a system that can be used for the training

of caregivers and the detection of harmful postures.

2 STATE OF THE ART

Manual movement of persons in need of care leads

to high mechanical stress in the lower back area of

caregivers (Weißert-Horn et al., 2014). Unfavorably

long-lasting trunk postures as well as lifting, holding

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

Assessing Postures and Mechanical Loads during Patient Transfers.

DOI: 10.5220/0010155300210029

In Proceedings of the 14th International Joint Conference on Biomedical Engineer ing Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 21-29

ISBN: 978-989-758-490-9

Table 1: Classiﬁcation of the upper body (UB) postures

into angle ranges (Freitag et al., 2007). Sagittal inclinations

above 60

◦

are classiﬁed as critical. Lateral incliniations and

torsions above |20|

◦

are also harmful.

UB limited un-

posture acceptable acceptable acceptable

sagittal 0

◦

-20

◦

-60

◦

>60

◦

lateral 0

◦

-|20|

◦

- >|20|

◦

torsion 0

◦

-|20|

◦

- >|20|

◦

and pulling large parts of the patient’s weight without

the use of aids such as lifts or sliding mats lead to a

compression of the intervertebral discs of the carers

up to 9 kN during this activity. This is far above the

upper limit of 3.4 kN for lumbosacral-disc compres-

sive forces (J

ager et al., 2013).

Freitag et al. reported that nurses working on a geri-

atric ward take an average of 1390 upper body incli-

nations above 20 degrees per work shift (Freitag et al.,

2007). Weißert-Horn et al. found similar results and

state that caregivers spend a total of two hours per

shift in a bent posture or bend down 1500 times per

shift (Weißert-Horn et al., 2014). Recurring loads can

cause pain and injuries in the back and upper extrem-

ities. It is therefore important to identify and avoid

harmful postures.

Movement and posture analysis systems based on

different types of sensors can be used to identify

harmful postures and to estimate the stresses and

strains that occur in the musculoskeletal system

of both the carer and the person being cared for.

Theilmeier et al. captured postures via an optoelec-

tronic motion capturing system and video cameras

(Theilmeier et al., 2010). Additionally, a care bed

was equipped with a framework attached to the

bedstead and connected to the bedspring frame

via force sensors at the four bed-corners. J

ager et

al. analyzed nine different activities with a patient

transfer in or at the bed or chair with the same

system. They found that the load on the lumbar spine

can be reduced through biomechanically optimized

transfer instead of conventional methods (J

ager et al.,

2013). Wei et al. described a system that uses data

from depth-image cameras to estimate the skeletal

poses and joint forces of wheelchair users during

transfers (Wei et al., 2018). A Microsoft Kinect V2

camera was used to record skeleton data during three

different care activities (Agrawal and Ertel, 2018).

Lin et al. used both inertial sensor technology and a

marker-based motion detection system on the patient

to evaluate the transfer performance by the nursing

staff (Lin et al., 2018). However, the possibilities of

ergonomic work design and assessment have hardly

been used systematically in nursing and health care

up to now (Ding et al., 2014). This points to the

necessary need for ergonomic work teaching systems

for caregivers.

One aspect of working in a back-friendly manner

is avoiding excessive upper body inclination angles

and working with a straight back. Thereby, the

movements of the upper body should be considered

in all three directions: Movements in the sagittal

direction mean the forward inclination of the trunk.

The lateral movement - away from the central axis -

is called a lateral inclination. The torsion of the upper

body between the thoracic spine and the lumbar

spine is deﬁned as torsion. Freitag et al. made a

classiﬁcation of the upper body postures into angle

ranges and their degree of risk (Freitag et al., 2007).

The classiﬁcation is listed in Table 1. Here angles

in the range of 0

◦

-20

◦

belong to the neutral position.

But according to the DIN standard DIN EN 1005-4

(German Institute for Standardization, 2005) lateral

inclinations and torsions above ten degrees are also

classiﬁed as critical.

In the literature are many recommendations for

back-friendly working in patient care (Michaelis

and Hermann, 2010), especially from professional

associations (Baum et al., 2012; Kusma et al., 2015).

In the present work, we focused on the transfer

from the bed edge to a wheelchair. Additionally to

our ”Healthcare Prevention System” (Brinkmann

et al., 2020), we developed a posture recognition

system based on a motion capture suit to identify

potentially harmful postures considering the rec-

ommendations for ergonomic working in the care

sector.

3 MATERIALS AND METHODS

In the following, the study design, the biomechanics,

and the used sensors and methods are described.

3.1 Study Design

To analyze typical care activities and their ergonomic

execution, we conducted a study with 13 caregiver

students, aged between 18 and 55 years (10 women,

3 men). In this article, we analyzed the transfer from

the edge of a bed to a wheelchair. The transfer was

carried out by each of the 13 students. Additionally

to the sensors, a Kinaesthetics teacher observed and

evaluated the performance of the care activity under

consideration of ergonomic working methods and ki-

naesthetic aspects. A member of the research staff

HEALTHINF 2021 - 14th International Conference on Health Informatics

acted as a patient so that no real patients were in-

volved. To avoid overloadings, the patient cooperates

during the treatment.

The test procedures were approved by the local ethics

committee (ethical vote: Carl von Ossietzky Univer-

sity Oldenburg and conducted in accordance with the

Declaration of Helsinki.

3.2 Applied Sensor Systems

Several sensor systems were used in the study and are

described in the following.

3.2.1 Motion Capture System

The caregiver students were equipped with a full-

body motion capture system of Motion Shadow

(Shadow, 2020). The system includes 17 motion

nodes. Figure 1 shows the human joints (blue

dots) and the positions of each motion node (green

boxes) of the motion capture system. A motion

node is a measurement unit, which includes a 3D-

accelerometer, 3D-gyroscope, and 3D-magnetometer.

The sampling rate of each sensor is 100 Hz.

3.2.2 Multi Kinect System

In addition, the nursing process was recorded with

a Multi Kinect System (Fifelski et al., 2018). This

system contains 4 Microsoft Kinect v2 depth cam-

eras. Each Kinect v2 is connected to an Intel NUC

Mini-PC, which acquires the data from the camera

and sends it to the main computer, where the data of

the four cameras is fused into a colored point cloud.

The depth cameras have to be registered to each other

Figure 1: Calculation of sagittal inclination angle: The total

angle α results from the segment angles SpineLow, Spine-

Mid and Chest.

Figure 2: Anatomy of human thigh. Electromyography

electrodes are on rectus femoris, vastus medialis and biceps

femoris. The dots on the muscles indicate the positions of

the electrodes. Image courtesy of Complete Anatomy (Es-

sential Anatomy 5) (3D4Medical, 2020).

to ensure that the four point clouds of cameras are

aligned. The Multi Kinect System enables a 3D view.

3.2.3 Electromyography

Surface Electromyography (EMG) was also used to

record the muscular activities associated with mus-

cle contraction in order to gain information on the

activation behavior of selected muscle groups during

the transfer from bed to wheelchair: vastus medialis

(VM), rectus femoris (RF), biceps femoris (BF) (see

Figure 2). Preparation, collection and processing pro-

tocols were consistent with SENIAM guidelines (Her-

mens et al., 1999). Signals of the bipolar surface elec-

trodes (14 mm diameter and 10 mm inter-electrode

distance, GE Medical/Hellige) were ampliﬁed with

2500 Hz by local ampliﬁers, then ﬁltered (bandpass

10–700 Hz) and sent to Biovision Inputbox. The es-

timation of muscle activity in the caregiving process

was based on the potential level and set in relation

to the chronological sequence of the caregiving ac-

tivities. The processing of the raw EMG signals was

done via rectiﬁcation, Root Mean Square (RMS) and

mean value.

3.3 Posture Analysis System

Figure 3 shows the system-workﬂow for processing

the IMU data of the motion capture system and au-

tomated analyzing and reporting risky postures dur-

ing care treatments. The IMU data is exported in

bvh-format and integrated into our system. The body

model deﬁnes an object-oriented data structure for

the motion data according to the recommendation of

the International Society of Biomechanics (Wu et al.,

2002). The model realizes access to the data of body

sections such as joints and realizes data related op-

erations. The identiﬁcation component reads the raw

Assessing Postures and Mechanical Loads during Patient Transfers

Figure 3: System-Workﬂow for processing the IMU data of the motion capture system and analyzing and reporting physical

loading during care treatments.

data streams and converts them into the structure of

the deﬁned data model. The identiﬁcation component

returns the data object with the joint positions and an-

gles. The analysis component uses the data object

for motion analysis and executes the analysis strategy.

We implemented an algorithm for the analysis of risky

postures during care activities based on the physical

posture model. The physical posture model contains

various threshold values, which indicate harmful pos-

tures. We implemented rules for the identiﬁcation of

risky postures regarding the classiﬁcation of (Freitag

et al., 2007) mentioned in Table 1.

Joint positions are measured based on the neutral-

zero method. According to the joints’ neutral position

within the different body planes, a predeﬁned perpen-

dicular is marked to measure the range of movement

(ROM) in degree. This procedure is most common

for peripheral joints of the lower and upper limbs. As-

sessing the ROM of the spine is more complex since

each vertebra is naturally located slightly different to

another. ROM increases with every involved segment.

Thus, the measurement of the spinal inclination

angle is challenging - especially in different postures.

In this approach, we decided to analyze the angle by

dividing the spine into three segments. The segments

in the skeleton model are called SpineLow, SpineMid,

and Chest. The upright neutral-zero position in the

sagittal plane is taken as the perpendicular which is in

this case identical to the longitudinal axis. To cal-

culate the sagittal inclination angle during transfer,

we shifted the perpendicular horizontally to L5, the

ﬁfth lumbar spine vertebra. A straight line between

L5 and C7 (which covers all relevant segments) in-

dicates the angle corresponding to the perpendicular,

whereby C7 is the seventh cervical vertebra. The sys-

tem’s calculation of the sagittal inclination angle is

illustrated in Figure 1. To estimate the angle between

L5 and C7, the angles of segments SpineLow, Spine-

Mid and Chest were considered to calculate the total

inclination angle. Lateral inclination angles, as well

as torsions, are calculated accordingly.

In summary, the analyzer executes the analysis algo-

rithms and returns the result object. We implemented

a rule-based algorithm in the analyzer component,

which monitors the upper body inclination and tor-

sion and the compliance of the threshold values of the

physical posture model. If a threshold value is ex-

ceeded, the posture is classiﬁed as potentially harm-

ful. The report (result object) is a 3D virtual ob-

ject, which shows the visualization of skeleton model

(similar to the skeleton models in Figure 4) during

the execution of the care activity and indicates harm-

ful posture by specifying which of the deﬁned rules

were exceeded.

3.4 Manual Patient Handling and

Biomechanics

The transfer from the edge of a bed to a wheelchair

can be divided into three main phases: Stand the pa-

tient up, turn the patient, and sit the patient down into

a wheelchair. In the following, the three main phases

and their subphases will be examined concerning

their ergonomic execution and biomechanics. Figure

4 shows the phases exemplarily for one student. In

the top of the Figure, 3D camera images of the Multi

Kinect System of each of the subphases are shown. In

the bottom of the Figure, the corresponding skeleton

models measured by the motion capture system are

visualized. At the beginning of the transfer, the

person requiring care sits at the edge of the bed. The

caregiver positions himself frontally to the patient

(stand). In the preparation phase, the caregiver gives

instructions to the patient and bends down to the

patient while squatting. The upper body remains

as straight as possible. The caregiver puts his arms

around the patient. The patient is also asked to put

his arms around the caregiver. In the lift phase,

the patient is lifted into a standing position by the

caregiver. At the end of the lift, the patient and the

caregiver stand in an upright position.

In the second main phase the caregiver rotates to-

gether with the patient in small cradle steps until the

patient is standing directly in front of the wheelchair.

The rotation in small steps prevents harmful upper-

HEALTHINF 2021 - 14th International Conference on Health Informatics

Figure 4: In the ﬁrst phase of the transfer from the edge of the bed to a wheelchair, the caregiver lifts the patient into a standing

position. In the second phase the caregiver turns the patient in small steps until the patient is standing directly in front of the

wheelchair. In the third phase the caregiver lowers the patient into the wheelchair.

body torsions.

In the third phase of the transfer the caregiver

carefully lowers the patient into the wheelchair. The

transfer ends with an upright standing position.

In a biomechanical view, manual patient handling

leads to a compression, ﬂexion and torsion of the

caregiver’s intervertebral discs depending on the

patient’s weight and the executed transfer mode

ager et al., 2013). Therefore, the intervertebral

discs are affected vertically by different compressive

forces. These forces are almost parallel to the ﬂat

geometry of the sacroiliac joints (SIJ) (see Figure 2),

which are the direct connection between pelvis and

sacrum and primarily responsible for load transmis-

sion to the hip joints and ﬁnally to the legs and vice

versa (Vleeming and Stoeckart, 2007). Additional

muscle groups are essential in order to transfer loads

isolated and effectively from the lumbar spine to

the pelvis and to compensate for potential overload

effects (Vleeming and Stoeckart, 2007). Effective

load transfer is achieved, when muscle forces cause

compression of the SIJ, and thereby, preventing

shearing of the joints (Richardson et al., 2002; van

Wingerden et al., 2004). Both the muscles of the

lower limb and the back extensors inﬂuence the SIJ

movements and its stabilization mechanisms via

lever arms (Vleeming and Stoeckart, 2007). In the

present study, the muscles of lower limb are analyzed.

During hip ﬂexion, torsional forces are transmitted to

the SIJ due to the connection of the rectus femoris

(RF) to the pelvis (Hammer et al., 2015) (see Figure

2). The rectus femoris (RF) is one of the extensors

of the knee and the ﬂexors of the hip (Garrett and

Kirkendall, 2000). The vastus medialis (VM) is also

involved in knee extension (Mansﬁeld and Neumann,

2008). It is assumed that increased activity of the RF

can lead to pain in the SIJ, and therefore may cause

lower back pain (Hammer et al., 2015). Furthermore,

due to the connection to the pelvis, the biceps femoris

(BF) performs knee ﬂexion and hip extension and

thus affects the SIJ.

4 RESULTS

4.1 Analysis of Inclination Angles

After focusing on the different phases of the transfer,

the sagittal and lateral upper body inclination as well

as the upper body torsion were examined. Figure

5 shows the inclination and torsion angles during

the transfer. The inclination angles were calculated

according to Figure 1. The main phases are marked

in the graph.

The transfer itself took about 30 seconds. As ex-

pected, the highest inclination angles occur in the

ﬁrst and last phase: stand up the patient and sit down

the patient. The ﬁrst phase begins with a stand in a

neutral position with inclination angles below 20

◦

The caregiver is only slightly inclined to the patient.

In the preparation phase, the caregiver squats. The

upper body also inclines in the sagittal direction.

In this preparation phase as well as during the lift,

angles of up to 40 degrees are reached, so that the

Assessing Postures and Mechanical Loads during Patient Transfers

Figure 5: Sagittal and lateral upper body inclination as well

as upper body torsion during the transfer from the edge of a

bed to a wheelchair.

limit to the neutral range (<20

◦

) is exceeded. The

range between 20

◦

-60

◦

is limited acceptable (cf.

Table 1). Therefore, the time spent in this potentially

harmful position should be as short as possible. Ad-

ditional loads acting on the body or the back in such a

posture are particularly associated with a risk of back

injuries. During lift phase, part of the patient’s weight

acts on the body. The stand the patient up phase

ends in an upright position. The upright position is

maintained during the rotation so that the sagittal

inclination angle remains in the neutral range during

the entire phase. The turning phase has three peaks.

The number of peaks indicates the number of double

steps executed during the rotation.

In the last phase, the caregiver bents again to lower

the patient into the wheelchair. Here, angles signiﬁ-

cantly above 20

◦

are achieved, which indicates again

a potentially harmful posture. After setting the patient

down, the caregiver returns to an upright position. As

already described, the system generates a result report

that shows the corresponding color-coded angles as

well as the visualization of the skeleton model during

the care activity. If the threshold values are exceeded,

the corresponding angle is marked in yellow (sagittal

inclination angle between 20

◦

-60

◦

) or red (sagittal

inclination angle >60

◦

) and the posture is classiﬁed

as potentially harmful.

The lateral inclination remains in an acceptable posi-

tion during the entire care activity. In the preparation

phase, the student takes a slightly right-bent posture

(positive inclination angles). During the lift and the

patient’s descent, the student shows a slight inclina-

tion to the left (negative inclination angles). During

the rotation, the angle is almost zero so that the stu-

dent has a straight upright position.

Figure 6: Mean muscle activity of vastus medialis (VM),

rectus femoris (RF), and biceps femoris (BF) during the

phases of the transfer.

A short torsion of more than 20

◦

can be seen at the

beginning of the recording and thus before the actual

care action. During the care procedure itself, the stu-

dent remains in an acceptable position. Based on this

diagram, a distinction can also be made between the

preparation phase and the lift. During the preparation

phase, the student is squatting and is inclined in the

sagittal direction. During the lift, the direction of tor-

sion and lateral inclination changes. Special attention

should be paid for coupled movements (torsion + in-

clination, sagittal + lateral inclination) since they can

be particularly harmful (Panjabi and White, 1990).

4.2 Analysis of Muscle Activities

To evaluate the speciﬁc caregiving phases, the ac-

quired data of the applied sensor systems are related

to the surface EMG data and analyzed and interpreted

according to biomechanical assumptions. Figure 6

shows the mean muscle activity of rectus femoris

(RF), vastus medialis (VM), and biceps femoris (BF)

during the phases of the transfer. The mean muscle

activity of RF, VM and BF in the squatting position

are 322 mV, 175 mV and 77 mV. While lifting the pa-

tient the muscle activities are 425 mV, 212 mV and

141 mV. Turning the patient leads to mean muscle ac-

tivities of 121 mV, 173 mV and 88 mV and while sit-

ting the patient down the mean muscle activities are

204 mV, 142 mV and 192 mV.

5 DISCUSSION

Caregivers experience high musculoskeletal loads

during their daily work, which leads to back com-

plaints and a high rate of absenteeism at work. It

HEALTHINF 2021 - 14th International Conference on Health Informatics

has been shown, that ergonomically correct working

can lead to a signiﬁcant reduction in musculoskele-

tal load (Brinkmann et al., 2020; Weißert-Horn et al.,

2014). However, the possibilities of ergonomic work

design and assessment have hardly been used system-

atically in nursing and health care up to now (Ding

et al., 2014). In consequence, we developed a posture

recognition system which is able to identify potential

harmful body postures according to the recommen-

dations for ergonomic working in the care sector and

integrated it into our Healthcare Prevention System

(Brinkmann et al., 2020). The posture recognition

system analyzes the data of a full-body motion cap-

ture system. A result report visualizes the skeleton

model together with color-coded information about

inclination and torsion angles during the care activ-

ity.

In the present work, we focused on the transfer from

the bed into a wheelchair and analyzed the posture

in terms of sagittal and lateral inclination angles as

well as torsions. We could show the concept of the

system and see its application mainly in the training

of nursing students. An advantage of this system is

the report and its visualization of the nursing handling

which allows a retrospective discussion of the activ-

ity with objective angle information. In addition, the

visualization of the skeleton model can be rotated, so

that the nursing activity can be evaluated from differ-

ent perspectives. The advantage of using a full-body

motion capture system over cameras is that the ﬁeld

of vision cannot be obscured and the system does not

require additional space. The disadvantages are that

the patient is not recorded and therefore context in-

formation may be missing.

The Multi Kinect System (Fifelski et al., 2018;

Brinkmann et al., 2020) was used to record the scene

by depth camera data. This system also allows a 3D

view and therefore a viewing from different perspec-

tives. We are currently working on merging skeletal

models from the Kinect data to make posture analysis

with this data as well. So the camera system and the

motion capture system can be used interchangeably

depending on requirements.

As it is not possible to determine loads on the basis

of pure body postures, further information is needed

to assess the risk potential for a care activity. Con-

textual information about the nursing action itself and

the weight of the patient can be used to make rough

estimations about mechanical loads, which act on

the caregiver’s body. For more precise load estima-

tions we used surface electromyography to analyze

the muscle activities during the patient transfer. The

higher activity of the VM in comparison to the RF

while squatting and lifting was also observed in other

studies (Brinkmann et al., 2020; Dionisio et al., 2008;

Garrett and Kirkendall, 2000; Slater and Hart, 2017)

and can be considered as realistic. The mean activ-

ity of VM were around 46% higher in the squat po-

sition and around 50% higher while lifting the pa-

tient in comparison to the RF. This is assumed to

be due to the RF’s biarticular function as hip ﬂexor

and knee extensor (Garrett and Kirkendall, 2000). In-

creased activity of the RF can lead to an increased

hip ﬂexor/extensor torque and may cause pain in the

SIJ, and therefore lower back pain (Garrett and Kirk-

endall, 2000; Hammer et al., 2015). Furthermore, the

mean muscle activity of the BF increases around 45%

during the squat ascent compared to static squatting.

This was also observed in previous studies (Garrett

and Kirkendall, 2000; Dionisio et al., 2008; Slater and

Hart, 2017). Moreover, Brinkmann et al. found in a

further study conducting three different dynamic lift-

ing tasks that the mean activity of the quadriceps and

hamstring musculature increases with lifting higher

loads (Brinkmann et al., 2021). Also, an intra- as well

as an interindividual similarity of EMG muscle acti-

vation pattern regarding time and shape of the signals

for the selected muscles of the lower limb could be

observed.

ACKNOWLEDGEMENTS

Funded by the Lower Saxony Ministry of Science

and Culture under grant number 11-76251-12-10/19

ZN3491 within the Lower Saxony “Vorab“ of the

Volkswagen Foundation and supported by the Center

for Digital Innovations (ZDIN). The development of

the Multi Kinect System was funded by the German

Federal Ministry of Education and Research (Project

No. 16SV8532).

REFERENCES

3D4Medical (2020). Essential anatomy 5 (5.0.8).

3d4medical, dublin, ireland. accessed 10.05.2019.

Agrawal, A. and Ertel, W. (2018). Automatic nursing care

trainer based on machine learning. In KHD@ IJCAI,

pages 53–59.

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

D. M., Busse, R., McKee, M., Bruyneel, L., Raf-

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

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

pital care: cross sectional surveys of nurses and pa-

tients in 12 countries in europe and the united states.

Bmj, 344:e1717.

Baum, F., Beck, B., Fischer, B., Gl

using, R., Graupner,

I., et al. (2012). Pr

avention von r

uckenbeschwerden;

Assessing Postures and Mechanical Loads during Patient Transfers

topas r–konzept der bgw f

ur pﬂege und betreu-

ung. Hamburg, Germany: Berufsgenossenschaft f

Gesundheitsdienst und Wohlfahrtspﬂege.

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

O., Diekmann, R., Isken, M., Fudickar, S., and Hein,

A. (2020). The aal/care laboratory – a healthcare pre-

vention system for caregivers. Nanomaterials and En-

ergy, 9(1):1–10.

Brinkmann, A., Fifelski-von B

ohlen, C., Hellmers, S.,

Meyer, O., Diekmann, R., and Hein, A. (2021). Physi-

cal burden in manual patient handling – quantiﬁcation

of lower limb emg muscle activation pattern of healthy

individuals lifting different loads ergonomically. In

HEALTHINF 2021.

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

loskeletal risks associated with nurses and nursing as-

sistants handling overweight and obese patients: A lit-

erature review. Work, 53(2):439–448.

Ding, J., Lim, Y.-J., Solano, M., Shadle, K., Park, C., Lin,

C., and Hu, J. (2014). Giving patients a lift-the robotic

nursing assistant (rona). In 2014 IEEE International

Conference on Technologies for Practical Robot Ap-

plications (TePRA), pages 1–5. IEEE.

Dionisio, V. C., Almeida, G. L., Duarte, M., and Hirata,

R. P. (2008). Kinematic, kinetic and emg patterns dur-

ing downward squatting. Journal of Electromyogra-

phy and Kinesiology, 18(1):134–143.

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. In

2018 International Conference on Computational Sci-

ence and Computational Intelligence (CSCI), pages

679–684. IEEE.

Freitag, S., Fincke, I., Dulon, M., Ellegast, R., and Nien-

haus, A. (2007). Messtechnische analyse von belas-

tenden k

orperhaltungen bei pﬂegekr

aften: eine geri-

atrische station im vergleich mit anderen kranken-

hausstationen. ErgoMed, 31:130–140.

Garrett, W. E. and Kirkendall, D. T. (2000). Exercise and

sport science. Lippincott Williams & Wilkins.

German Institute for Standardization (2005). Safety of ma-

chinery - human physical performance - part 4: Eval-

uation of working postures and movements in relation

to machinery; german version. DIN EN 1005-4:2005.

Hammer, N., M

obius, R., Schleifenbaum, S., Hammer, K.-

H., Klima, S., Lange, J. S., Soisson, O., Winkler, D.,

and Milani, T. L. (2015). Pelvic belt effects on health

outcomes and functional parameters of patients with

sacroiliac joint pain. PloS one, 10(8).

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

Blok, J., Rau, G., Disselhorst-Klug, C., and H

agg, G.

(1999). European recommendations for surface elec-

tromyography. Roessingh research and development,

8(2):13–54.

ohmann, U., Lautenschl

ager, M., and Schwarz, L. (2016).

Belastungen im pﬂegeberuf: Bedingungsfaktoren, fol-

gen und desiderate. Pﬂege-Report, pages 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 biomechani-

cal stresses on caregivers’ upper extremities and low

back. Human factors, 61(7):1125–1140.

ager, 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. Annals of occupational hygiene, 57(4):528–

544.

Kliner, K., Rennert, D., and Richter, M. (2017). Gesund-

heit und Arbeit-Blickpunkt Gesundheitswesen: BKK

Gesundheitsatlas 2017. MWV Medizinisch Wis-

senschaftliche Verlagsgesellschaft.

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

Fischer, K., Schmidt, J., Behl-Sch

on, S., and Pohrt,

U. (2015). Der pﬂege das kreuz st

arken - individu-

alpr

avention r

ucken bei der berufsgenossenschaft f

gesundheitsdienst und wohlfahrtspﬂege. Trauma und

Berufskrankheit, 17(4):244–249.

Lin, C., Kanai-Pak, M., Maeda, J., Kitajima, Y., Naka-

mura, M., Kuwahara, N., Ogata, T., and Ota, J. (2018).

Translational acceleration, rotational speed, and joint

angle of patients related to correct/incorrect methods

of transfer skills by nurses. Sensors, 18(9):2975.

Mansﬁeld, P. and Neumann, D. (2008). Essentials of Kine-

siology for the Physical Therapist Assistant. Mosby.

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

pﬂegekonzepts r

uckengerechter patiententransfer in

der kranken-und altenpﬂege. Langzeit-Follow-up zur

Ermittlung der Nachhaltigkeit pr

aventiver Effekte.

Schriftenreihe der Bundesanstalt f

ur Arbeitsschutz

und Arbeitsmedizin (F2196). Dortmund.

Panjabi, M. M. and White, A. (1990). Clinical biomechan-

ics of the spine. Lippincott Philadelphia, PA.

Richardson, C. A., Snijders, C. J., Hides, J. A., Damen, L.,

Pas, M. S., and Storm, J. (2002). The relation be-

tween the transversus abdominis muscles, sacroiliac

joint mechanics, and low back pain. Spine, 27(4):399–

405.

Shadow (2020). Motion Workshop, Motion Capture Sys-

tem, www.motionshadow.com, accessed: 28.04.2020.

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

terns during different squat techniques. Journal of

strength and conditioning research, 31(3):667–676.

Theilmeier, A., Jordan, C., Luttmann, A., and J

aGer, M.

(2010). Measurement of action forces and posture to

determine the lumbar load of healthcare workers dur-

ing care activities with patient transfers. Annals of

occupational hygiene, 54(8):923–933.

van Wingerden, J.-P., Vleeming, A., Buyruk, H., and

Raissadat, K. (2004). Stabilization of the sacroiliac

joint in vivo: veriﬁcation of muscular contribution to

force closure of the pelvis. European Spine Journal,

13(3):199–205.

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, pages 113–

137. Elsevier.

Wei, L., Tsai, C.-Y., and Koontz, A. M. (2018). The rela-

tionship between joint ranges of motion and joint ki-

HEALTHINF 2021 - 14th International Conference on Health Informatics

netics during sitting pivot wheelchair transfers. Pro-

ceedings of the Conference of the Rehabilitation En-

gineering and Assistive Technology Society of North

America.

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

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

gonomisch richtige arbeitsweise beim transfer von

schwerstpﬂegebed

urftigen. Zeitschrift f

ur Arbeitswis-

senschaft, 68(3):175–184.

Wu, G., Siegler, S., Allard, P., Kirtley, C., Leardini, A.,

Rosenbaum, D., Whittle, M., D D’Lima, D., Cristo-

folini, L., Witte, H., et al. (2002). Isb recommendation

on deﬁnitions of joint coordinate system of various

joints for the reporting of human joint motion—part

i: ankle, hip, and spine. Journal of biomechanics,

35(4):543–548.

Assessing Postures and Mechanical Loads during Patient Transfers