Evaluating a Multi Depth Camera System to Consolidate Ergonomic

Work in the Education of Caregivers

Conrad Fifelski-von Böhlen

, Anna Brinkmann

, Sebastian Fudickar

Sandra Hellmers

and Andreas Hein

Assistive Systems and Medical Technologies, Carl von Ossietzky University, Ammerländer Heerstraße 140,

Oldenburg, Germany

sandra.hellmers@uol.de, andreas.hein@uol.de

Keywords: Nursing Education, Ergonomics, Teaching, Optical Devices, Feedback, Case Studies.

Abstract: Through the demographic change in Western European countries the demand for nurses in elderly care rises.

Additionally, constant high physical stresses causing nurses to leave the profession before the retirement age

and having a high number of sick leave days. To reduce the physical strain, focus must be set to ergonomically

correct work in nursing schools. Currently given technical infrastructure in the schools lacks the capability to

provide nursing instructors with analyzable data from simulated care acts. In this work, we present and

evaluate the Multi-Kinect-System, our custom developed depth sensor system for recording and analyzing

care acts. In a study, 13 students of a nursing school performed a simulated transfer tasks under observation

of a nursing instructor and our system. The instructor gives a more in-depth evaluation of the transfer when

using the intuitively analyzable data of our system, regarding the feedback length and information content.

1 INTRODUCTION

1.1 Demographic Background

The percentage of elderly people in Germany

increases in the next decades. In 2060 there will be

22.3 million people over 65 years, what is 33 % of the

German population. In comparison, in the year 2000

they made up only 17 % (Destatis, 2019). This

demographic change leads to an increasing demand

for care services, severing the situation in the nursing

institutions, which are overbooked even today (Kliner

et al., 2017; Weißert-Horn et al., 2014). Additionally,

the everyday work of the caregivers is accompanied

by constant physical and psychological overload. For

example, patient transfers are regarded as one of the

main factors responsible for back pain for elderly care

professionals (Weißert-Horn et al., 2014), leading to

a high amount of sick leave, compared to other

medical professions (Kliner et al., 2017), see figure 1.

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

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

https://orcid.org/0000-0002-3553-5131

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

Figure 1: Days of incapacity to work, due to

musculoskeletal diseases for employees in medical

professions (Kliner et al., 2017).

The impact of physical stress in this understaffed

profession is enormous, because a vicious circle

forms. High physical loads results in injuries, which

are the reason for sick leave, what reduces the staff

Böhlen, C., Brinkmann, A., Fudickar, S., Hellmers, S. and Hein, A.

Evaluating a Multi Depth Camera System to Consolidate Ergonomic Work in the Education of Caregivers.

DOI: 10.5220/0010186500390046

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 39-46

ISBN: 978-989-758-490-9

and increases the pressure on the remaining staff.

Therefore, a reduction of the physical load for

caregivers is necessary to prepare the profession for

the aging society. A significant reduction of the

musculoskeletal stresses of nurses is achievable by an

ergonomically correct method of transferring patients

(Brinkmann et al., 2020; Jäger et al., 2014).

Furthermore, there is an urgent need for training the

use of care equipment (Rashidi and Mihailidis, 2013).

Even the usage of accessible care aids, like an ant-slip

mat during a manual patient transfer can measurably

reduce the physical load on the lower back (Jäger et

al, 2014).

1.2 Technology-based Approach

Typically, the nursing instructor attends the care acts

and gives feedback afterwards, possibly with visual

data from one standard camera. However, because of

the different poses, nurses are taking during care acts,

choosing different view angles is desirable. The usage

of an optical system that provides multiple viewpoints

and occlusion compensation for the analyzing nursing

instructor is desirable. A combination of multiple

depth cameras provides these features and to change

the point of view in recordings at any time.

Additionally, a three-dimensional scene is captured,

containing more information than a standard camera

image. An in-depth analysis of the care act is possible

with such a system. The care instructor and the

apprentice nurse have a better insight in the

ergonomics of the care transfer and can work together

on improvements. However, the technology-based

approach with the usage of multiple depth cameras

must be evaluated.

1.3 Related Work

Detecting and enhancing the compliance level of

health care professionals like nurses with best

practices in minimizing job-related back pain is a key

research field (Zhao et al., 2016). Studies track

specific activities of nurses that might increase the

risk of lower back pain injuries, like lifting and

pulling (Reichold et al., 2017; Zhao et al., 2016), by

videos or instructors by hand. From a technical point

of view, the depth camera Microsoft Kinect v2 is

evaluated regarding the accuracy of the depth image

and the point cloud data (Yang et al., 2015; Fifelski et

al., 2018; Fankhauser et al., 2015). The LiveScan3D

by Kowalski et. al. (Kowalski et al., 2015) is a system

that combines the depth images of multiple Kinects

V2 to display colored point clouds. Markers and the

Iterative Closest Point (ICP) algorithm (Besl and

McKay, 1992) are used to combine the generated

point clouds. It is shown that this system can be used

to scan a human head precisely enough to create 3D

printed figurines. The idea of training nurses in

specific scenarios or simulation-based is a common

and evaluated practice, for example in resuscitation

(Roh et al., 2013). Furthermore, the application of

technology in the education is not limited to acquire

data or providing scenarios. Approaches to analyze

training with algorithms, processing the gathered data

are possible (Reichold et al., 2017; Lins et al., 2018;

Lins et al., 2019). In order to assure a more distinct

evaluation of physical demanding nursing tasks, we

propose a System for multiview recording, with the

aim to provide high quality data for the nursing

instructor. As most algorithms are just estimating

positions of bodies or poses in the observed scenes,

the analysis of a care expert is necessary anyway. The

Multi-Kinect-System in this work provides higher

frame rates and is capable to process a larger area of

interest, compared to the mentioned approaches.

2 MATERIALS AND METHODS

2.1 Multi-Kinect-System

For the pose and movement analysis an optical

system provides the necessary data. Here the

combination of four Microsoft Kinect v2 depth

cameras is used. In three-dimensional, colored point

clouds it is possible to view the scene from indefinite

adjustable view angles. Because the point clouds are

merely three-dimensional objects, they can be rotated

or translated during visualization in real time or

offline. This is beneficial while observing care acts

like transfers, due to the possibility to see poses and

movements from different sides. A depth camera can

be occluded by obstacles or humans in the scene. A

combination of several depth cameras can

compensate the occlusion. Additionally, the point

clouds of multiple depth cameras can be combined to

a single point cloud. This increases the density of the

data and covers scenes from different sides. The depth

cameras are situated around the bed and are focusing

the center of the bed. All four cameras are installed

on tripods at 1.8 m and cover an area of

approximately 2 m * 2m. They tilted towards the

ground at 45 degrees. Research by Fankhauser et. Al

(Fankhauser et al., 2015) indicates that the best

distance from the point of interest to the camera is

around 0.8 to 1.2 meters. Also, the depth accuracy

deteriorates in the corners of the depth image

(Fankhauser et al., 2015). Therefore, the placement of

HEALTHINF 2021 - 14th International Conference on Health Informatics

the cameras needs to follow these findings. Each

Kinect v2 is connected to a mini pc via USB 3.0 that

runs Windows 10. The Kinect v2 Software

Development Kit (SDK) only runs on Windows

machines. Because the whole room network is ROS-

based the minicomputers are communicating to a

master computer running Ubuntu 16.04 and ROS via

private Ethernet connections. A server network PCI

card must be installed to handle the traffic. Contrary

to standard RGB cameras, the Microsoft Kinect v2 is

able of acquiring a depth image alongside with color

information. Although it is also possible to obtain

other data streams like infrared and a coarse body

estimation, we focus on depth and color information.

The depth information is obtained at a rate of 30 Hz

with 512 * 424 pixel, while the color image features

1920 * 1080 Pixel at 30 Hz. The Kinect v2 SDK

provides tools to map the color information to the

depth image. In short, all color pixels are discarded

for regions which are not in the depth image. This

reduces the resolution of the color image. The depth

data must be processed with a static Look-up-Table

(LUT), internal saved by each camera, in order to

calculate point clouds. This is necessary because each

Kinect v2 has slightly different lens distortion

parameters. The mapped color image stream and the

point cloud can be combined to a colored point cloud

on the master computer, see figure 2. The colored

point clouds are displayed for the analysis to the

nursing instructor.

Figure 2: The generation of point clouds. First, the depth

image and LUT are combined to a colorless point cloud.

Afterwards the color information is added. The resulting

colored point cloud of 512 * 424 with 4 bytes each point

must be computed 30 times per second per camera.

Another step, that must be done before using the

systems, is to register the four depth cameras to each

other. The 3D registration can be done in different

ways with printed patterns or objects of a special

shape. We use a 15 cm radius Styrofoam sphere. The

sphere is placed on at least three different spots in the

view of all cameras. This allows to calculate the

relative position from one camera to another. In the

visualization of the four aligned point clouds, the user

can adjust the viewpoint. Additionally, we

implemented a player that can be used like a standard

video player for point clouds, see figure 3. Even in the

recordings and when the recorded point cloud is

paused, it is still possible to change the point of view

at any time.

Figure 3: The point cloud player window. Its pixelated

appearance is caused by the depth camera resolution of 512

* 424. It is possible to play (1), stop (2), pause (3), rewind

(8), forward (9) or record (4) point clouds. The live button

(5) switches from recording to live data. To reduce the size

of the recorded files it is possible to apply voxel grid and

statistical outlier filters (6). The coarse body estimation can

be turned on, too (7).

2.2 Case Study

The study "stark" (Studie zur Transferanalyse

rückenschonender Pflegekonzepte) (Study for

Transfer Analysis of back-sparing Care Concepts) is

designed to compare traditional with multiview 3D

imaging feedback rounds. The study is approved by

the ethical board of the Carl von Ossietzky University

Oldenburg (Drs.EK/2019/004).The research question

is whether it is possible to identify the impact of the

Multi-Kinect-System on the second feedback round.

There are 13 study participants involved. To evaluate

a transfer regarding the Kinesthetic care conception

(Maietta and Hatch, 2011) and ergonomic ways of

work, typically, the nursing instructor supervises the

transfer and gives feedback afterwards. This

procedure can be enhanced and digitized through our

complex Multi-Kinect-System. To evaluate our

Multi-Kinect-System in the field, audio is recorded.

The recordings are done in a case study where a

nursing instructor uses the system while educating

nurses. This study compares feedback with and

without the usage of the Multi-Kinect-System. A

nursing instructor articulates which function of the

Multi-Kinect-System point cloud player should be

used, i.e. what the instructor wants to see. The indirect

usage reduces the influence of the usability of the

system. These commands are fulfilled by the study

organizer on the recording computer. The recordings

are containing key words, indicating the use of the

Multi-Kinect-System. The observed keywords are

"stop", "rewind", "forward", "turn view angle" and

words with a similar semantic. Furthermore, the time

Evaluating a Multi Depth Camera System to Consolidate Ergonomic Work in the Education of Caregivers

of the feedback rounds is compared, and the criticized

aspects are tracked. These aspects are body balance,

knee and lunge positioning, back, stand, usage of

caretakers movement resources and the whole

movement sequence. The transfer from a care bed to

a wheelchair is the observed task, the study

participant must fulfill. The patient is a 28-year-old

woman weighting 63 kg and already sitting on the

bed's edge. The patient is acting like a movement

impaired elderly person with abdominal stability

while standing. To accomplish the transfer correctly,

the participant must use the movement resources of

the patient, the care equipment and the learned

knowledge how to transfer movement impaired

people. Because the participants are differing in

height, strength and expertise, there is not one pattern

solution. Nevertheless, the participant should choose

the right grips and poses to apply forces according to

the nature of the patient’s body. This study relies on

nurses of the Evangelische Altenpflegeschule e.V.

Oldenburg nursing school. Although the participants

are still in the school courses, they are working in

nursing homes or similar institutions as caregivers.

Some of them are in the courses for further education

to obtain a higher qualification in the profession.

Therefore, they have a lot of practical experience and

are already instructed how to use their body and the

care equipment correctly. Nevertheless, they are

making mistakes during the transfer task, too.

Figure 4: The study setup. A wheelchair is situated beneath

a care bed. The transfer is carried out on the force

measurement plate in front of the bed.

Overall, 13 participants performed the transfer task.

The age of the 10 women and 3 men ranges from 18

to 55 years. There are no participants with physical

restrictions. The participants are entering the study

location and are prepared for the transfer task. In

addition to the examined Multi-Kinect-System, a few

other sensors are used, observing the performance of

the participant for other research interests. These

sensors are an electromyograph, a sensor suit (Motion

Workshop, 2020) and a ground reaction force plate

(AMTI, 2016). None of the used sensors are

impairing the movement of the participant. However,

this work focuses on the evaluation of the Multi-

Kinect-System. The wheelchair is placed on the right

side besides the care bed. Because the force

measurement plate elevating the participant 12 cm

over the ground, the wheelchair is always elevated on

a socket, see figure 4. The nursing instructor

supervises each transfer by visual inspection, but

without the data of the Multi-Kinect-System at first.

This feedback is recorded. Afterwards the same

transfer is examined again, but this time with the

Multi-Kinect-System. To see the data, the participant

and the instructor are gathering around the recording

computer. This second feedback round is recorded

again. The goal is to find and evaluate the differences

between the both feedback rounds. The comparison is

essential to derive how the sensor system extends the

perception of the nursing instructor.

Figure 5: The study procedure. Direct dependencies are

marked with solid lines and indirect ones with dotted lines.

The Multi-Kinect-System operator (MKS Operator)

provides the digital data and is therefore indirectly involved

in the digital feedback. Additionally, the nurse can review

the own performance in the digital feedback round and

therefore understand and react to critics.

Observing the own performance in the scenario

might be educational for the nurse. The study

organizers are just involved as passive participants

(DeWalt and DeWalt, 2011). The three-dimensional

data is presented to the nurse and the nursing

instructor. A study organizer is operating the system,

following their commands by changing the zoom or

the viewpoint and pausing, rewinding or forwarding

the recorded data. Figure 5 depicts the procedure. The

resulting data set, which must be analyzed, consists

of two audio recordings per study participant.

HEALTHINF 2021 - 14th International Conference on Health Informatics

3 RESULTS

The data gathered during the feedback rounds with

and without the Multi-Kinect-System is audio data.

Therefore, the task is to designate features, which are

differing in both feedback rounds. The following

features are relevant: the duration, special keywords,

which are indicating the use of the Multi-Kinect-

System and the proposed improvements. Each of the

13 study participants is represented by a three-digit

number. Note, that the order of the numbers in the

following graphs and tables is not the order in which

they performed the transfer task.

3.1 Duration of the Feedback round

Based on the point cloud recordings, the feedback

round with the use of the Multi-Kinect-System must

take at least longer than the feedback without the

system to reason the usefulness of the system. We

assume that a higher duration of a feedback round

indicates a possible presence of additional

information. However, the information of the both

feedback rounds is examined later in addition to the

plain duration here. Table 1 compares the duration of

the audio recording with and without the Multi-

Kinect-System. Figure 6 depicts these differences in

the duration.

Table 1: The duration of the analog (TA) and digital (TD)

feedback rounds for each study participant (SP) in minutes.

TA average is 0:59, TD average is 2:16.

Figure 6: The different time duration of the feedback rounds

in seconds for each Study participant.

3.2 Keywords in the Feedback round

with the Multi-Kinect-System

The audio recordings are indicating the use of the

Multi-Kinect-System. There are keywords like

"stop", "rewind", "forward", "turn view angle" and

words with a similar semantic, which can be

quantified. Table 2 shows the occurrences of the

keywords for each study participants, who are

represented by a three-digit number.

Table 2: The occurrences of keywords (#K) for each study

participant (SP).

3.3 Amount of Criticized Aspects

The overall aim is still to improve the caregivers

movements, respecting Kinaesthetics (Maietta and

Hatch, 2011) care conception and ergonomic

approaches. Therefore, it is necessary to compare the

improvements and the critic, the nursing instructor

suggests in the both feedback rounds. The six aspects,

which are the most important for a healthy transfer

regarding the caregiver, are examined. These aspects

were chosen according to the nursing instructor:

 body balance (BAL)

 knee and lunge positioning (LNG)

 usage and form of the back (BAK)

 stand on the ground (STD)

 usage of the caretakers movement

resources (RES)

 movement sequence (MVM)

Body balance is important during the transfer to

control the weight of the caretakers at all time. To

shift the load from the back to the lower extremities a

lunge position alongside with the right knee angle is

desirable. Both, the back and the neck should be

stretched, so that the lifting is not performed with a

bent or twisted spine. The most relevant body part is

the back, as the loss of working time is caused by back

pain. This is also related to the positioning of the feet

to get a stable stand. A too narrow stand reduces the

stability. Because all transfers should take the

caretakers movement resources into account, to avoid

unnecessary loads, this aspect is mentioned during the

feedback rounds. The right movement patterns and

sequences should avoid unfavorable activities like the

torsion of the spine. In the first feedback round, the

audio is recorded, and the criticized aspects can be

counted. Afterwards the transfer is observed using the

Multi-Kinect-System. The following table 3 presents

0 s

50 s

100 s

150 s

200 s

250 s

142

243

424

471

483

107

280

303

350

366

315

437

473

Analog Feedback Digital Feedback

SP 142 243 424 471 483 107 280 303 350 366 315 437 473

TA 0:57 0:43 0:59 0:36 1:00 1:09 1:41 1:29 0:38 1:10 0:38 1:01 0:46

TD 4:06 2:58 2:01 2:09 1:55 1:28 2:07 3:37 2:43 1:27 1:07 2:33 1:20

SP 142 243 424 471 483 107 280 303 350 366 315 437 473

#K 3332151 1 2 1 1 21

Evaluating a Multi Depth Camera System to Consolidate Ergonomic Work in the Education of Caregivers

the occurrences of the mentioned critics for each

study participant (SP) for both feedback rounds.

Table 3: The criticized aspects in the analog and digital

feedback. The values are separated by a slash. BAL: body

balance, LNG: knee and lunge positioning, BAK: back,

STD: stand, RES: usage of the caretaker’s movement

resources, MVM: movement sequence. Average of SUM

(sum) of analog feedback round is 2.31 (overall 30) and

average of digital feedback round is SUM is 3.38 (overall

44). SP is the study participant.

SP BAL LNG BAK STD RES MVM SUM

142 1/1 0/2 0/1 1/2 1/0 1/2 4/8

243 0/1 0/0 0/0 0/0 0/2 0/1 0/4

424 0/1 1/0 1/1 0/0 1/1 0/1 3/4

471 1/1 1/0 0/2 0/0 1/0 0/0 3/3

483 1/1 0/0 0/0 0/0 1/1 0/0 2/2

107 1/1 0/1 0/1 0/0 0/0 1/0 2/3

280 2/2 2/2 1/3 0/1 0/0 0/1 5/9

303 0/0 1/0 1/1 0/1 0/0 0/0 2/2

350 0/0 1/0 1/2 0/1 0/0 1/0 3/3

366 0/0 1/0 0/0 0/0 0/0 1/0 2/0

315 0/0 0/0 0/0 0/ 0/1 0/0 0/1

437 1/1 1/1 0/1 0/0 1/1 0/0 3/4

473 0/1 1/0 0/0 0/0 0/0 0/0 1/1

3.4 Information Content

The plain duration of the feedback solely is no prove

regarding the information content. However, when

using the feedback duration as an indicator for a more

in-depth debriefing, while also considering the

amount of criticized aspects in the transfer, denser

information can be assumed while using the Multi-

Kinect-System. The sum of the criticized aspects is

2.31 with analog feedback and 3.39 with digital

feedback on average, while the average analog

feedback time is 59 seconds and digital is 2 minutes

and 16 seconds. For example, for the study

participant 142, the feedback time increased by 3

minutes and 9 seconds between analog feedback and

the feedback with the Multi-Kinect-System. Dividing

the average feedback time by the average amount of

criticized aspects, the result is 25 seconds per

criticized aspect for analog feedback and 40 seconds

per criticized aspect for digital. This can either

indicate that less time per mentioned aspect is needed

in the analog feedback or the digital feedback round

is more in depth through the possibilities of the Multi-

Kinect-System. However, the amount of mentioned

or criticized aspects should always have more priority

than duration concerns. Note that the indirect usage

of the Multi-Kinect-System also consumes a one-

digit number of seconds per participant.

3.5 Discovery of Aspects in the Digital

Feedback

Looking into the data of the point cloud player for

study participant 107, additional aspects regarding the

lunge positioning and one additional aspect regarding

the usage of the back were discovered by the nursing

instructor. The usage of the Multi-Kinect-System

makes additional aspects visible in this and other

cases. Because the analog feedback is always the first

to take place, it is more likely, that issues, discussed

in the analog feedback, are not mentioned again in the

digital feedback round. Indeed, the amount of

criticized aspects in table 3, is higher in some classes

for some of the study participants, than in table 4.

However, the overall count of criticized aspects

increases significantly between analog and digital

feedback rounds.

4 DISCUSSION

The different findings in the feedback rounds are

significant and must be researched further.

Additionally, the care instructors need training to use

the proposed software on their own, without further

help. A possible impact on the overall usefulness of

the system and the benefit for the nursing instructor

and the student seems to be the usability. Although

there are no complaints about the data quality of the

depth data, it is planned to improve the technical

parameters of our Multi-Kinect-System. Improvable

aspects are resolution, accuracy and usability with the

use of new hard and software. Furthermore,

additional studies must be established with more

participants and specialized Kinaesthetics (Maietta

and Hatch, 2011) trainers. More education facilities

should be involved in testing our system in their

education program. This helps to install this system

in the everyday education of nurses in elderly care,

probably increasing their competences and helping

nursing instructor to detect unfavorable poses and

movements in the education process earlier.

Additionally, the usage of automatic pose evaluation

algorithms (Stoffert, 1985) is planned to support the

instructor’s feedback. More in-depth analysis of

characteristics of unfavorable poses could also lead to

a better understanding why they are common. A

future goal is to integrate our system as an inherent

part in the education of nurses.

HEALTHINF 2021 - 14th International Conference on Health Informatics

5 CONCLUSIONS

A case study was executed for the evaluation of our

Multi-Kinect-System to quantify the benefits of

technology-based education enhancements. In this

first approach, we use participating observation and

audio recordings during two types of feedback rounds

after a care scenario, completed by the study

participants. One feedback is given by a nursing

instructor without advanced visual 3D recording data

and one feedback is given with this data. These

feedback rounds are differing significantly in various

parameters. We identified meaningful features, which

are indicating the usefulness of our system in the

education of nurses in elderly care.

ACKNOWLEDGEMENTS

This work is carried out in the research project

ITAGAP, funding number: 02L14A240 of the

Bundesministerium für Bildung und Forschung –

BMBF (German Federal Ministry for Education and

Research) at the Evangelische Altenpflegeschule e.V.

Oldenburg (Evangelical Nursing School).

REFERENCES

AMTI, 2016. Accupower force platform, accessed: 2020-

03-18. URL

http://www.accupowersolutions.com/force-platforms.

Besl, P.J., McKay, N.D., 1992. A method for registration of

3-d shapes. In IEEE Transactions on Pattern Analysis

and Machine Intelligence 14 (2), 239-256,

https://doi.org/10.1109/34.12179.

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

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

2020. The AAL/Care Laboratory – A Healthcare

Prevention System for Caregivers. In Nanomaterials

and Energy 9,1, 27-38,

https://doi.org/10.1680/jnaen.19.00021.

DeWalt, K.M., DeWalt, B.R., 2011. Participant

Observation: A guide for fieldworkers, Alta Mira.

Plymouth.

Fankhauser, P., Bloesch, M., Rodriguez, D., Kaestner, R.,

Hutter, M., Siegwart, R., 2015. Kinect v2 for mobile

robot navigation: Evaluation and modelling. In 2015

International Conference on Advanced Robotics

(ICAR). Istanbul, 388-394,

https://doi.org/10.1109/ICAR.2015.7251485.

Destatis, 2019. Federal office of statistics website,

accessed: 2020-03-18. URL

https://service.destatis.de/bevoelkerungspyramide/#!y

=2050.

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

Hein, A., 2018. Multi depth camera system for 3d data

recording for training and education of nurses. In 2018

International Conference on Computational Science

and Computational Intelligence (CSCI). Las Vegas,

NV, 679-684,

https://doi.org/10.1109/CSCI46756.2018.00137.

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

S., Nienhaus, A., Luttmann, A., 2014. Analyse der

Lumbalbelastung beim manuellen Bewegen von

Patienten zur Prävention biomechanischer

Überlastungen von Beschäftigten im

Gesundheitswesen. In Zentralblatt für Arbeitsmedizin,

64, 98-112,

https://doi.org/10.1007/s40664-013-0010-4

Maietta, L., Hatch, F., 2011. Kinaesthetics Infant Handling,

Hans Huber. Bern, 2

edition.

Kliner, K., Rennert, D., Richter, M. (Eds), 2017.

Gesundheit und Arbeit – Blickpunkt Gesundheitswesen.

Medizinisch Wissenschaftliche Verlagsgesellschaft.

Berlin.

Kowalski, M., Naruniec, J., Daniluk, M., 2015. Livescan3d:

A fast and inexpensive 3d data acquisition system for

multiple kinect v2 sensors. In 2015 International

Conference on 3D Vision. Lyon, 318-325,

https://doi.org/10.1109/3DV.2015.43.

Lins, C., Eckhoff, D., Klausen, A., Hellmers, S., Hein, A.,

Fudickar, S., 2019. Cardiopulmonary resuscitation

quality parameters from motion capture data using

differential evolution fitting of sinusoids. In Applied

Soft Computing Journal 79, 300-309,

https://doi.org/10.1016/j.asoc.2019.03.023.

Lins, C., Klausen, A., Fudickar, S., Hellmers, S., Lipprandt,

M., Röhrig, R., Hein, A., 2018. Determining

cardiopulmonary resuscitation parameters with

differential evolution optimization of sinusoidal curves.

In Proceedings of the 11th International Joint

Conference on Biomedical Engineering Systems and

Technologies, Oldenburg, 665-670,

https://doi.org/10.5220/0006732806650670.

Motion Workshop, 2020. Motion capture system - shadow

motion sensor suit, accessed: 2020-03-18. URL

http://www.motionshadow.com.

Rashidi, P., Mihailidis, A., 2013. A survey on ambient-

assisted living tools for older Adults. In IEEE Journal

of Biomedical and Health Informatics 17 (3), 579-590,

https://doi.org/10.1109/JBHI.2012.2234129.

Reichold, J., Agrawal, A., Thurlings, M., Cohen, I., Weber-

Fiori, B., Rölle, A., Hassan, M., Dürr, M., Pfeil, U.,

Pape, A.-A., Grünert, G., Schmidt, A., Pfeil, M., Fäßler,

Jauch, V.V., Reiterer, H., Winter, M., Ertel, W.,

Eberhardt, J., 2017. Human-machine interaction in

care-education. In Burghardt, M., Wimmer, R., Wol,

C., Womser-Hacker C. (Eds), Mensch und Computer

2017: Tagungsband. Books on Demand. Norderstedt,

351-356, https://doi.org/10.18420/muc2017-ws08-

0311.

Roh, Y.S., Lee, W.S., Chung, H.S., Park, Y.M., 2013. The

effects of simulationbased resuscitation training on

nurses' self-efficacy and satisfaction. In Nurse

Evaluating a Multi Depth Camera System to Consolidate Ergonomic Work in the Education of Caregivers

Education Today 33 (2), 123-128,

https://doi.org/10.1016/j.nedt.2011.11.008.

Stoffert, V., 1985. Analyse und Einstufung von

Körperhaltungen bei der Arbeit nach der Owas-

Methode. In Zeitschrift für Arbeitswissenschaft 1, 31-

38.

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

Raske, H.-W., Landau, K., 2014. Ergonomisch richtige

Arbeitsweise beim Transfer von

Schwerpflegebedürftigen. In Zeitschrift für

Arbeitswissenschaft 68 (3), 175-184,

https://doi.org/10.1007/BF03374443.

Yang, L., Zhang, L., Dong, H., Alelaiwi, A., Saddik, A.E.,

2015. Evaluating and improving the depth accuracy of

kinect for windows v2. In IEEE Sensors Journal 15 (8),

4275-4285,

https://doi.org/10.1109/JSEN.2015.2416651.

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

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

Niederriter, J., Luo, C., Luo, X., 2016. A privacyaware

kinect-based system for healthcare professionals. In

2016 IEEE International Conference on Electro

Information Technology (EIT), Grand Forks, ND, 205-

210, https://doi.org/10.1109/EIT.2016.7535241.

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