Assessing Occupational Health with a Cross-platform Application based

on Self-reports and Biosignals

Sara Silva, Catia Cepeda, Jo

ao Rodrigues, Phillip Probst and Hugo Gamboa

LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics),

Faculdade de Ci

encias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal

Keywords:

Occupational Risks, Mobile Application, Sensors, Surveys, Assessment, Ergonomic.

Abstract:

Occupational disorders have a signiﬁcant impact on the health of ofﬁce workers. This has even more relevance

considering the increased population in this work modality and the recent shift to remote work. Efforts are

needed to create worker awareness and reduce occupational hazards. Based on this motivation, an intuitive and

easy to use application for the assessment of occupational risks was developed and it is presented in this paper.

This application records risk factors in the biomechanical, psycho-social, and environmental domains through

data collected with self-assessment tools and wearable sensors, contributing to a more complete, robust and

personal assessment of risk exposure. This article presents the system architecture and its application interface.

Examples of interaction with each module of the app are also provided.

1 INTRODUCTION

Work-related Diseases (WRDs) have signiﬁcant im-

plications for workers’ well-being and quality of life,

productivity, and absenteeism, thus having a big so-

cial and economic impact. According to data released

by the International Labor Organization (ILO) (Pe-

runi

c, 2008), around 2.3 million men and women

worldwide die annually from accidents or work-

related illnesses. The most recent European Union

Survey in 2013 revealed that three-ﬁfths (60.1%) of

all people who reported work-related health problems

had musculoskeletal disorders (Wahlstr

om, 2005).

This was followed by problems related to stress, de-

pression, and anxiety (15.9%), headaches and/or eye

fatigue (4.8%), and cardiovascular diseases (4.5%)

(Eurostat, 2017). In recent years, an increase of de-

pendence on the use of computers to carry out daily

work tasks has been observed. Thus, it is expected

that the number of workers affected by WRD’s will

increase. Currently employed techniques for the as-

sessment and evaluation of risk exposure have limita-

tions. Observational techniques like the Owako Work

Posture System (OWAS) (Hellig et al., 2018), Upper

Limb Rapid Assessment (RULA) (McAtamney and

Nigel Corlett, 1993), and Rapid Ofﬁce Strain Assess-

ment (ROSA) (Sonne et al., 2012) need to be eval-

uated by experts and require a long time to be im-

plemented. These techniques also measure workers’

exposure to risk using a risk score value based on a

standard worker. This means that individual variabil-

ity among workers, such as anthropometric variations,

age, work experience, exercise practice, among oth-

ers, is not taken into account. Furthermore, these in-

struments focus on documenting and quantifying the

postures and behaviors observed during work, focus-

ing on risk as a problem from a physical point of

view. However, research suggests that environmental

and psycho-social factors also contribute to the onset

of WRDs, thus indicating that the etiology of WRDs

consists of multiple contributing factors. Therefore,

deriving the ergonomic risk associated with a worker,

by solely basing it on physiological factors is in-

sufﬁcient as a complete analysis of the risk factors

that contribute to an overall risk score (Santos et al.,

2020).

In order to reﬂect the multifactorial nature of

WRDs, we propose an application for monitoring

a variety of dimensions in relation to occupational

health. The application is structured around three

potential risk domains, namely risks associated with

psycho-social, biomechanical, and environmental fac-

tors. Each domain offers a multitude of assessments,

that are considered as mini-apps, that collect data

from the user by either employing self-assessment

tools, such as questionnaires or by utilizing the sen-

sors that are integrated into mobile devices, smart-

watches, and wearable physiological sensors. The ap-

Silva, S., Cepeda, C., Rodrigues, J., Probst, P. and Gamboa, H.

Assessing Occupational Health with a Cross-platform Application based on Self-reports and Biosignals.

DOI: 10.5220/0010846700003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 549-556

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

549

plication is intended to be intuitive, easy-to-use and

accessible to everyone. Using this tool should help

make people more aware of their working conditions,

identify sources of risk factors and provide exposure

reports to ergonomic risks, thus contributing to a more

complete, robust and personal assessment of risk ex-

posure. With this, the user has access to information

that can help in understanding which risk factors are

present, adapt their behavior and prevent the onset of

occupational diseases.

The proposed application is being developed in

the context of the PrevOccupAI (BiosignalsLibphys,

2020) project, which has as the main objective the

promotion of occupational health through the iden-

tiﬁcation of occupational risk and contribution to its

mitigation. The project is carried out in collaboration

with Portuguese public entities such as the Direc¸

Geral da Sa

ude (DGS) and the Autoridade Tribut

aria

e Aduaneira (AT). Therefore the ﬁrst version of the

application was produced in Portuguese.

This manuscript explores the creation of the ap-

plication describing the system architecture and the

technological systems used in data acquisition. The

interface of the application is also presented.

2 RELATED WORK

Several tools that aim to assess occupational risk in

the ofﬁce work context are found in the literature.

These are mainly based on observational techniques

that consist in a visual inspection of the physical load

at work and/or by means of self-reporting, reﬂecting

the worker’s perception of physical and biomechani-

cal exposure (David, 2005).

One such tool is the Rapid Ofﬁce Strain Assess-

ment (ROSA), developed by Sonne et al. in 2012

(Sonne et al., 2012). It is an instrument to assess

the occupational risk of desk work with computer us-

age based on observational assessment and provide

information to the user about the necessary changes

based on reports of worker discomfort. For the de-

velopment of this tool, the postures outlined in the

CSA Z412 guidelines for ofﬁce ergonomics, devel-

oped by a group of experts, were used. This method

is constructed so that risk factors can be grouped into

several different sections, namely chair, monitor, tele-

phone, keyboard, and mouse. The score is assigned to

each risk factor representing an individual posture and

equipment, and modeled based on deviations from a

deﬁned neutral posture given by the CSA. The ﬁnal

score is obtained by combining scores from all sub-

sections. The ROSA has a ﬁnal score ranging from

1 to 10, which is divided into three risk levels, where

each successive score means an increase in the pres-

ence of risk factors.

The European Agency for Safety and Health at

Work (EU-OSHA) has launched a qualitative occu-

pational risk assessment tool called Online Interac-

tive Risk Assessment (OiRA) (European Agency for

Safety and Health at work (EU-OSHA), 2019). OiRA

is a web application that provides a set of risk assess-

ment tools for various work sectors, including the of-

ﬁce work sector. This tool assesses the risks associ-

ated with the ofﬁce workstation, ofﬁce layout, ofﬁce

environment, workers, and work organization. The

questionnaires are in a simple format and are orga-

nized to provide the user with information about haz-

ards in the work area, which the user assesses. After

completing the questionnaire, an action plan is gen-

erated with recommendations on how to reduce the

risk and a schedule of when measures against possi-

ble risks should be implemented. In the end, a report

is produced that contains all the results obtained dur-

ing the evaluation.

In addition to these, MUEQ (Maastricht Upper

Extremity Questionnaire) (Eltayeb et al., 2007) is a

reliable and consistent self-report tool that assesses

the occurrence and nature of CANS (Arm, Neck and

Shoulder Complaints) in ofﬁce workers. The tool has

the advantage of combining physical, psycho-social,

and environmental factors in one assessment.

All these tools provide relevant solutions for the

evaluation of occupational exposure to several do-

mains. The proposed application is inspired by these

solutions and integrates several of their ideas to pro-

vide a more complete assessment. The integration of

such ideas also come from the microErgo concept,

which suggests that the combination of several risk

factors, associated with their incidence factor can be

made to provide an intuitive risk score and increase

the perception of the user over how to change his/her

working environment to improve his/her health in the

long run (Rodrigues et al., 2021). This work goes be-

yond the presented literature by (1) combining mul-

tiple dimensions of risk factors, such as biomechani-

cal, psycho-social and environmental; (2) provide an

accessible, easy-to-use and personal tool for occupa-

tional assessment and (3) using both direct and qual-

itative measures to quantify all dimensions of the oc-

cupational exposure.

3 SYSTEM ARCHITECTURE

Figure 1 presents an overview of the application’s sys-

tem architecture. The backbone of the application

consists of a content and a form server that are in-

HEALTHINF 2022 - 15th International Conference on Health Informatics

550

stalled on an Amazon Web Services (AWS) virtual

machine. Both servers are used to create the content

that is displayed within the application. By employ-

ing the servers as the content providers it is possible to

offer a cross-platform approach that allows for large

parts of the application to be accessed by either a mo-

bile device or a desktop PC.

The application is structured in a modular way, so

that each assessment is considered as a mini-app that

targets speciﬁc ergonomic risk factors. Depending on

the type of data acquired, the application integrates

Surveys and Sensing mini-apps. Survey mini-apps are

used to acquire information about the users through

questionnaires. These surveys could be either psycho-

logical, biomechanical, or of environmental nature.

Sensing mini-apps are used to acquire biosignal, mo-

tion, or environmental data from the subjects during

work.

All the included elements are described and ex-

plained throughout this section.

Figure 1: System architecture.

3.1 AWS Cloud

The application is hosted on the Amazon EC2 in-

stance web server, with a minimal Linux operating

system. On this web server, we installed and conﬁg-

ured WordPress as a content server.

WordPress (WordPress, 2016) is an open-source

content management platform that can create web-

sites, blogs, or applications. This system is based on

PHP with a MySQL database running on a server. The

application layout can be organized from widgets or

by editing PHP or HTML codes. WordPress facil-

itates the integration of new content in the applica-

tion by non-programmers users, which is an impor-

tant functionality given that the project has multidis-

ciplinary collaborators.

Since this application has mini-apps based on

forms, a Form server was also installed and conﬁg-

ured: LimeSurvey. LimeSurvey (LimeSurvey, 2009)

is a free and open-source online survey application

software written in PHP. With LimeSurvey it is easy

to create, develop and publish online surveys and col-

lect responses from them, without doing any coding

and scripting. The system provides statistical analysis

based on survey responses. The generated question-

naires can be public or with controlled access.

Both WordPress and LimeSurvey use a MySQL

database. Data related to WordPress, including its

users, and data related to LimeSurvey, including the

output of the surveys, are all stored on the virtual ma-

chine.

3.2 Cross-platform Functionalities

The questionnaires implemented in LimeSurvey are

embedded as an iFrame into the HTML document

produced by WordPress. Since WordPress is hosted

on a web server, it is possible to access it through both

a mobile device or a desktop PC, thus allowing the

user to choose the device of their choice according to

their circumstances and needs. Using a desktop PC,

the surveys mini-apps can be simply accessed by us-

ing any browser. For android mobile devices, a native

app was developed that accesses the content on the

web server through an Android WebView component

that allows to embed a browser into an application.

This WebView is limited to just access the web server

and is integrated into the application in such a way

that users are not aware that they are using a browser.

In addition to survey mini-apps, the mobile appli-

cation offers sensing mini-apps that run locally on the

mobile device to acquire data from external devices.

These mini-apps are accessed through a JavaScript in-

terface that was embedded into WordPress. The Inter-

face checks if the website is accessed through a mo-

bile device that has the application installed and if this

is true, then the sensing mini-app is launched locally

on the phone. The process was implemented in such

a way that the transition between WebView and lo-

cal sensing mini-app is seamless and the user is not

aware of which content is run in the web or locally on

the mobile device. The next section describes what

data could be acquired by the sensing mini-apps and

explains how the systems are integrated into the app.

3.3 Android Native Components

The presented system is prepared to acquire data from

a smartphone, a smartwatch and from a wearable

device that acquires real-time electromyography, the

muscleBAN (muscleBAN, 2020). The combination

of these three devices includes a wide range of sen-

sors, which are presented in Table 1. These sensors

Assessing Occupational Health with a Cross-platform Application based on Self-reports and Biosignals

551

Table 1: Onboard sensors on devices.

Sensor Category Android OS Wear OS muscleBAN

Accelerometer Motion X X X

Linear Accelerometer Motion X

Gravity Motion X

Gyroscope Motion X X

Rotation Vector Motion X

Signiﬁcant Motion Motion X

Step Detector Motion X

Step Counter Motion X

Magnetometer Position X X X

Proximity Position X X

Ambient Temperature Environment X

Light Environment X X

Pressure Environment X X

Relative Humidity Environment X

Temperature Environment X

Microphone Environment X

Optical Heart Beat Special X

Electromyography Special X

are useful to evaluate ergonomic risks, such as the

worker posture, his psychological state and also to

evaluate the working environment.

The acquisition of data from these sensors is, at

this moment, only available with smartphones and

smartwatches running on an Android operating sys-

tem.

Figure 2: Sensing mini-apps acquisition system.

The acquisition of the aforementioned devices is

currently achieved by two different means as shown

in Figure 2. Mobile device internal sensors and the

muscleBAN data are acquired through OS Lite server.

OS Lite server is an experimental module developed

by PLUX (PLUX, 2021) that allows for data acqui-

sition from multiple PLUX devices and Android in-

ternal sensors at the same time. Acquired data is

stored on a local folder as .txt ﬁles. OS Lite also al-

lows for uploading the acquired data to a google drive

folder. This is done by compressing the folder that

holds the acquired data into a .zip ﬁle and then using

the Google Drive API to upload the ﬁle to a deﬁned

google drive repository. As OS Lite server does not

allow for the acquisition of the sensors that are inte-

grated into a smartwatch, a module was implemented

within the android application that makes this possi-

ble. The module was written in such way that the

acquired data is written to .txt ﬁles that are saved into

the same folder that OS Lite server uses. Thus, the

smartwatch data is sent to the google drive repository

together with the other sensor data.

4 USER INTERFACE

To create a ﬁrst version of the application, several

mini-apps were integrated considering the various

classes of risk that made up this occupational health

control system, ﬁlling in the gaps and ﬂaws found

in other existing tools. The main objectives of this

ﬁrst version is to: (1) include more than one mini-app

associated to the main three domains (biomechanics,

environment and psycho-social) and (2) gather most

of the existing risk factors in ofﬁce work. The as-

sessment mini-apps are able to collect information re-

garding risk exposure through questionnaires or sen-

sors provided by the acquisition systems, as it was de-

scribed in the previous section, depending on the risk

module selected. The questionnaires come from ex-

isting tools, available and validated in the Portuguese

HEALTHINF 2022 - 15th International Conference on Health Informatics

552

Table 2: Mini-apps included in the application ﬁrst version and respective assessment tool, organized by category.

(Sonne et al., 2012);

(Lima and Coelho, 2011);

(de Souza, 2012);

(European Agency for Safety and Health at work

(EU-OSHA), 2019);

(Silva et al., 2000);

(Eltayeb et al., 2007);

(Miranda, 2015);

(Sim

oes, 2016).

Category Mini-App Assessment Tool

Biomechanical Ofﬁce Design ROSA

; Lima and Coelho’s Checklist

Equipments ROSA

; Lima and Coelho’s Checklist

Pain Nordic Musculoskeletal Questionnaire

Posture Smartphone; Smartwatch; muscleBAN

Environmental Workstation OiRA

Ofﬁce Organization and Privacy Lima and Coelho’s Checklist

Noise Smartphone

Psycho-social Labor Requirements COPSOQ II

; MUEQ

Work Organization and Content COPSOQ II

; Work Design Questionnaire

Social Relations and Leadership COPSOQ II

General Well-Being COPSOQ II

Values in the Workplace COPSOQ II

Personality An adaptation of the Big Five Inventory

language, to determine exposure to the hazards posed

by the mini-app.

The application structure, including the general

risk categories, the mini-apps that assess each of

them, and the tools that each mini-app is based on,

is presented in Table 2.

Figures 3a, 3b and 3c show an example of user

interaction with the application for a psycho-social,

biomechanical and environmental assessment, re-

spectively.

At the home page, the three main categories are

presented: psycho-social, biomechanical, or environ-

mental assessment. In Figure 3a, the user selects

the Psycho-social Assessment (Avaliac¸

ao Psicosso-

cial) and the app presents the mini-apps that evalu-

ate different risk factor categories (see ”Mini-App” of

”Psyco-social” category in Table 2). In this example,

the user wants to evaluate how he is exposed to the

risk of WRDs due to the demand of the work, the im-

possibility of deciding or planning breaks, or even a

reduced or non-existent number of intervals. Bearing

this in mind, he selects the mini-app Labor Require-

ments (Exig

encias Laborais). This example shows a

survey mini-app, created with LimeSurvey, that the

worker ﬁlls in the questionnaire and submits the an-

swers that are automatically stored in the database.

Figure 3b explains how to carry out the postural

assessment, which is a mini-app of the Biomechani-

cal category (Avaliac¸

ao Biomec

anica). The user ac-

cesses the posture mini-app (Postura), in which are

presented information about how to setup the sen-

sors (Como montar os sensores?) and what data

is acquired during this procedure (O que ir

a ser

adquirido?). When the user is ready, he presses

the button to start the acquisition (Comec¸ar Agora).

Then, the application shows the time of acquisition

and also the connected devices, from which data is ac-

quired. This acquisition can be interrupted anytime by

the user, by clicking the button Terminar Avaliac¸

ao.

The data will be available in the end a Google Drive

folder.

The last example, presented in Figure 3c, uses the

microphone of the smartphone to record environmen-

tal noise (Ru

ıdo Ambiente), which is part of the En-

vironmental Assessment (Avaliac¸

ao Ambiental). Af-

ter selecting that mini-app, the user could verify what

data is acquired during this procedure (O que ir

a ser

adquirido?) and then start the acquisition (Comec¸ar

Agora). While acquiring the noise level, the decibel

value is displayed in real-time. This acquisition can

be interrupted anytime by the user, by clicking the

button Terminar Avaliac¸

ao. The data will be available

in a Google Drive folder.

5 CONCLUSIONS AND FUTURE

WORK

In this article, we intend to present and describe an

application that goes beyond the current available

tools, by combining various dimensions of risk fac-

tors through different assessment tools to provide a

broader and more robust risk assessment, contribut-

ing for a greater user awareness and promoting the

decrease of the occupational risk. The examples pro-

vided in this manuscript show that the user has the

ability to decide over which assessments he/her wants

to perform and are more adjusted to the user’s goals

or needs.

The integration of assessments based on sensors

suitable for monitoring certain risk factors is an as-

Assessing Occupational Health with a Cross-platform Application based on Self-reports and Biosignals

553

(a) Example of the steps to do a psycho-social assessment.

(b) Example of the steps to do a biomechanical assessment.

Figure 3: Application interface in three different examples. Additionally it is shown which parts of the application are run in

the WebView and which are run locally on the mobile device.

HEALTHINF 2022 - 15th International Conference on Health Informatics

554

set, since these techniques are more accurate and po-

tentially more reliable than the others (David, 2005).

Furthermore, using sensors instead of observational

methods promotes time-efﬁcient assessments that do

not rely on video inspection. Also, creating a com-

bined system that uses direct methods and ques-

tionnaires to assess occupational exposure represents

progress and improvements compared to the tools that

have already been developed.

Ethical and privacy issues may be regarded while

deﬁning the mini-apps that integrate the acquisition

of data from sensors. For example, data from the

microphone could expose environmental risk factors

while being acquired during the whole usage of the

application, however continuous audio recordings at

the workplace arise signiﬁcant privacy concerns, and

therefore, the way this information is stored and the

periods at which it is acquired, should be well deﬁned.

In the future, there are three main objectives: (1)

calculate the user’s ergonomic risks; (2) provide rec-

ommendations to reduce them; and (3) create users’

proﬁles to (a) keep historical records of users and (b)

extrapolate the assessment to new similar cases.

To calculate the scores from the information gath-

ered in each mini-app, the data obtained from the re-

sponses of the questionnaires and from the sensing as-

sessments is processed and combined to obtain a sin-

gle risk measure converted into a well-deﬁned scale,

as proposed by (Rodrigues et al., 2021). In addition

to this, other risk factors that constitute the variabil-

ity between workers, such as age, gender, alcohol and

smoking habits must also be included in the applica-

tion.

It would be extremely important to present the re-

sults of each mini-app to the user in a way that is eas-

ily interpretable and allows them to be made aware of

their exposure to WRDs and the risks that contribute

to them. According to a study (Silva, 2012), these

assessments have proven to increase worker comfort

and reduce risk factors in their ofﬁces. Associated

with this, it is planned to incorporate a set of recom-

mendations that can be presented to the user based on

the identiﬁed risks.

With the continuous acquisition of data over time,

historical information can be stored and used to create

a more detailed and robust assessment of the occupa-

tional exposure experienced by the user. In addition,

with historical records of multiple users, it is possible

to create relevant proﬁles. Newly incoming users that

ﬁt into these can have better answers even when little

data is available.

ACKNOWLEDGEMENTS

This work was partly supported by Science and Tech-

nology Foundation (FCT), under the project PRE-

VOCUPAI (DSAIPA/AI/0105/2019). The authors

have no conﬂicts of interest to report.

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