ABSTRACT

The prevention of work-related musculoskeletal disorders (WMSDs) is critical in industrial

settings, especially in tasks involving repetitive motion and awkward postures, such as those

found on assembly lines. This study aims to evaluate ergonomic risks associated with manual

operations in an assembly line using the Rapid Upper Limb Assessment (RULA) method under

real working conditions. To achieve accurate and continuous posture monitoring, a commercial

wearable motion sensor system was employed. The sensor data allowed for objective assessment

of joint angles and posture over extended periods without disrupting the workflow. By

integrating RULA scoring with sensor outputs, high-risk postures were identified, particularly in

the shoulders, neck, and upper limbs. The results revealed significant ergonomic risks in specific

tasks, highlighting the need for workstation redesign and job rotation strategies. This approach

demonstrates the viability and effectiveness of using commercially available sensor technologies

to conduct real-time ergonomic assessments and support proactive interventions in industrial

environments.

iv
 ACKNOWLEDGEMENT

I would like to express our gratitude and heartfelt appreciation to our

esteemed Founder Chancellor and President Col. Prof. Dr. R. RANGARAJAN
B.E (EEE), B.E (MECH), M.S (AUTO), DSc., and Foundress President
Dr. SAGUNTHALA RANGARAJAN, M.B.B.S., for providing us with an
ambient learning experience at our institution.

I take great pride in expressing our heartfelt appreciation to our beloved

Vice Chancellor Prof. Dr. RAJAT GUPTA, Ph.D., Registrar
Dr. E. KANNAN, Ph.D., and Dean Academics Dr. RAJU SHANMUGAM,
Ph.D., for encouraging us to complete our project and serving as inspiration for us
to perform well.

It gives me great pleasure to recognize the assistance and contributions of

our Head of the Department Dr. S. JAYAVELU, Ph.D., for his encouragement
and unwavering support throughout the duration of this project.

I would like to express my gratitude to my Project Supervisor

Dr. D. RAJAMANI, Ph.D., for his constructive inputs and constant
encouragement throughout this project.

I would like to thank Project Reviewer Hari Sivasri Phanindra. K, Ph.D.,

for their suggestions and comments throughout the course of this project.

I thank our department faculty, supporting staff, family members and my

batch mates for their help and guidance in completing this project.

(R.RAJKUMAR)
 TABLE OF CONTENTS
CHAPTER TITLE PAGE NO.
NO.

ABSTRACT

1 INTRODUCTION

1.1 Introduction 1

1.2 Background on workplace Ergonomic risk 1

1.3 Summary 2

1.4 Objective and scope of the review 2

2 LITERATURE REVIEW

2.1 Introduction to Ergonomics in industrial work 4

2.2 Traditional method of Ergonomic Risk Assessment 4

2.3 Advancement in ergonomics: sensor - based assessment 5

2.4 Integration of RULA with motion sensor 5

2.5 Limitation current research 5

2.6 Justification for the present study 6

3 IDENTIFIED RESEARCH GAPS 7

4 PROPOSED METHODOLOGICAL FRAMEWORK

4.1 Study Design 13

 4.2 Participant 13

4.3 Data collection tools 14

4.4 Data Collection procedure 14

4.5 Data processing and analysis 15

4.6 Validation and reliability 15

4.7 Ethical consideration 16

4.8 Outcome and recommendation 16

4.9 Limitation 16

5 SYNTHESIZED CONCLUSIONS FROM EXISTING

RESEARCH
5.1 Ergonomic risk are prevalent in assembly line work 17

5.2 RULA: an effective and practical tool for ergonomic risk 17

screening
5.3 Limitation of observation-based ergonomic assessment 17

5.4 Wearable sensor enhancing objectivity and continuity of 18

assessment
5.5 Integration of sensor data with RULA improve assessment 18
accuracy
5.6 Challenges and gaps in real-world application 18

5.7 Underutilization of continuous monitoring and feedback 19

mechanisms
REFERENCES
 LIST OF ABBREVIATIONS

ABBREVIATION DESCRIPTION

AI Artificial Intelligence

BBS Behavior-based safety

EHS Environmental Health and Safety

OSHA Occupational Safety and Health Administration

PPE Personal Protective Equipment

ROI Return on Investment

REBA Rapid Entire Body Assessment

RULA Rapid Upper Limb Assessment

ILO International Labor Organization

 CHAPTER-1

INTRODUCTION

1.1. INTRODUCTION

In manufacturing and assembly line operations, workers often perform repetitive tasks that require
prolonged physical exertion, which can result in poor posture and excessive strain on the
musculoskeletal system. These conditions are known to contribute significantly to the development
of work-related musculoskeletal disorders (WMSDs), which are among the most common
occupational injuries. The identification and assessment of ergonomic risks are crucial for improving
worker health and productivity.

One of the most widely used methods for evaluating ergonomic risk in the workplace is the Rapid
Upper Limb Assessment (RULA), a tool designed to evaluate upper limb posture and its associated
risks. However, traditional methods for conducting RULA assessments are often subjective and
limited by the availability of time and resources. This project seeks to overcome these challenges by
integrating a commercial motion sensor system to continuously monitor worker posture in real time
during regular work operations. The sensor technology enables objective, continuous, and
unobtrusive data collection on worker posture, making it possible to assess ergonomic risk more
accurately and efficiently. This study focuses on assessing the ergonomic risk in an assembly line
environment, where workers are required to perform tasks involving repetitive movements, bending,
and holding awkward postures for prevalence in logistics, manufacturing, construction, and
healthcare settings. Numerous studies have linked prolonged physical exertion, repetitive motion,
and poor ergonomic design to increased injury risk, reduced productivity, and long-term health
complications.

1.2 BACKGROUND ON WORKPLACE ERGONOMIC RISKS

Work-related musculoskeletal disorders (MSDs) are among the most prevalent and costly
occupational health issues globally. These disorders affect muscles, nerves, tendons, ligaments,
joints, and spinal discs, often resulting from prolonged exposure to physical risk factors in the
workplace. Industries such as manufacturing, construction, healthcare, and agriculture are
particularly vulnerable due to the nature of tasks involving heavy lifting, repetitive motions, forceful
exertions, and sustained awkward or static postures. The World Health Organization (WHO) and the

1
International Labour Organization (ILO) estimate that MSDs account for over one-third of all
occupational illnesses, leading to significant worker absenteeism, reduced productivity, and
increased healthcare costs.

Ergonomic risk factors contributing to MSDs include repetitive motion, high force demands,
vibration exposure, insufficient rest breaks, and poorly designed workstations or tools that force
workers into unnatural postures. For example, assembly line workers performing repetitive hand
movements or healthcare workers frequently lifting patients are at high risk. The chronic nature of
MSDs means symptoms often develop gradually, making early detection and prevention critical to
avoid long-term disability.

In response to this significant burden, ergonomic risk management has become a central focus in
occupational health research, policy, and practice. Effective management involves identifying risk
factors through workplace assessments, redesigning work environments and tasks, implementing
assistive devices or automation, and promoting worker training on safe practices. Advances in
wearable technology and sensor systems are also enabling more precise monitoring of physical strain
in real-time, offering new opportunities for proactive intervention.

1.3 SUMMERY

This project focuses on assessing ergonomic risks in an assembly line setting using the Rapid
Upper Limb Assessment (RULA) method, supported by data from a commercial motion sensor.
Traditional ergonomic evaluations are often limited by subjectivity and observation constraints. By
integrating wearable sensor technology, this study enables real-time, objective posture monitoring
during actual work conditions. The collected data allows for accurate RULA scoring, helping
identify high-risk movements and postures that could lead to musculoskeletal disorders. The findings
highlight critical ergonomic issues in specific tasks, suggesting the need for design improvements
and preventive interventions.

1.4 OBJECTIVES AND SCOPE OF THE REVIEW

The primary objective of this study is to evaluate ergonomic risks associated with assembly
line operations by applying the Rapid Upper Limb Assessment (RULA) method under real working
conditions, supported by data from a commercial motion sensor. The study aims to identify and
assess high-risk postures commonly adopted by workers, integrate wearable sensor data with RULA
to provide objective and continuous ergonomic evaluations, analyze the frequency and duration of

2
postures that may lead to musculoskeletal disorders (MSDs), and recommend ergonomic
improvements for workstation design and task organization. The scope of this review is limited to
manual operations within a typical industrial assembly line setting. It involves real-time observation
and data collection using motion sensor technology during normal working hours, application of
RULA for ergonomic risk scoring, particularly focusing on the upper limbs, neck, and trunk—and
analysis of data from multiple workers to uncover trends and common risk factors. The review is
restricted to physical ergonomic risks, with psychosocial and organizational factors outside its scope.
By focusing exclusively on measurable physical movements and real-time posture analysis, this
study offers a data-driven approach to enhancing occupational health and safety in assembly line
environments.

3
 CHAPTER 2

LITERATURE REVIEW
2.1 INTRODUCTION TO ERGONOMICS IN INDUSTRIAL WORK

Ergonomics is the scientific study of designing tasks, workspaces, and systems to fit the
capabilities and limitations of the human body. In industrial environments, ergonomics plays a
critical role in preventing workplace injuries, enhancing productivity and improving worker comfort.
One of the main areas of concern in industrial ergonomics is Work-Related Musculoskeletal
Disorders (WMSDs), which are injuries affecting muscles, tendons, ligaments, nerves, and joints,
often caused by repetitive tasks, awkward postures, and static loads (Bernard, 1997).

Assembly line operations pose a significant ergonomic challenge. Workers are often required to
perform repetitive, forceful, or precision-based tasks, frequently in constrained postures. These
conditions increase the risk of WMSDs in the upper limbs, neck, and back. Studies have consistently
shown that a large portion of absenteeism and reduced worker efficiency in manufacturing
environments can be traced back to poor ergonomic conditions (Punnett & Wegman, 2004).

2.2 TRADITIONAL METHODS OF ERGONOMIC RISK ASSESSMENT

To mitigate ergonomic hazards, various tools and observational techniques have been
developed for evaluating risk levels. One of the most widely used tools is the Rapid Upper Limb
Assessment (RULA), developed by McAtamney and Corlett (1993). RULA is a screening tool
designed to evaluate postures of the neck, trunk, and upper limbs, focusing particularly on static
muscle work and forceful exertions.

RULA assigns scores based on observed joint angles and posture combinations, culminating in an
overall risk score that guides whether ergonomic interventions are necessary. It is appreciated for its
simplicity, ease of use, and quick evaluation time. However, traditional RULA relies heavily on
visual observation, which introduces subjectivity and is limited to short time frames. It also lacks the
capability to evaluate dynamic posture changes or collect long-term data for trend analysis.

4
2.3 ADVANCEMENTS IN ERGONOMICS: SENSOR-BASED ASSESSMENT

To address the limitations of manual observation, researchers have turned to technology-

driven ergonomic assessments. Wearable motion sensors and Inertial Measurement Units (IMUs)
have become valuable tools in ergonomics due to their ability to measure motion and orientation in
real-time. These sensors record data such as joint angles, body posture, acceleration, and movement
patterns, allowing for continuous and objective ergonomic analysis.

Recent studies, such as Yang et al. (2020), have demonstrated the potential of these devices in
industrial environments. They allow for ergonomic risk assessments to be conducted during actual
work tasks, avoiding interruptions and collecting more representative data. In practice, this approach
reduces observer bias and enhances the ability to monitor postural risks throughout the workday.
Commercially available sensor systems like Xsens, Noraxon, and Kinetisense are already being used
in applied research and industry. They offer portable, non-intrusive solutions that can be integrated
with ergonomic analysis tools.

2.4 INTEGRATION OF RULA WITH MOTION SENSORS

A promising direction in ergonomics research is the combination of sensor data with

traditional assessment tools like RULA. This integration allows for real-time, automated scoring of
postures using pre-programmed RULA algorithms fed by sensor input. This hybrid approach
enhances the accuracy, consistency, and efficiency of risk assessments. Schall et al. (2018)
implemented a sensor-based framework that automatically calculated RULA scores in a
manufacturing setting. Similarly, Huysamen et al. (2019) used a wearable IMU system to collect
movement data and integrated it into posture scoring systems to detect high-risk postures throughout
a work shift. These studies confirm that sensor-integrated RULA assessments are not only feasible
but more reflective of real-world risks, as they account for the frequency and duration of risky
postures—something manual observation cannot reliably capture.

2.5 LIMITATIONS OF CURRENT RESEARCH

Despite significant advancements in ergonomic assessment technologies, notable gaps

persist in the current body of research. Many studies involving sensor-based ergonomic evaluations
are conducted in controlled laboratory settings, which may not accurately reflect the complexities

5
and challenges of real-world workplaces. Additionally, these studies often focus on short observation
periods, limiting their ability to capture the variability and cumulative effects of postures over an
entire work shift. Further limitations lie in the frequent use of custom-built or highly specialized
sensor systems that, while effective, are often cost-prohibitive or technically complex, making them
impractical for widespread industrial adoption. Moreover, there is a scarcity of research that employs
commercial, off-the-shelf motion sensors in active industrial environments such as assembly lines,
where tasks are fast-paced, repetitive, and ergonomically demanding. This highlights a clear need for
research that bridges the gap by applying accessible, reliable sensor technology in real work
conditions, using standardized assessment tools like the RULA method to produce practical and
scalable ergonomic insights.

2.6 JUSTIFICATION FOR THE PRESENT STUDY

This study directly addresses the limitations identified in previous research by utilizing
a commercially available motion sensor system to evaluate ergonomic risks among assembly line
workers using the Rapid Upper Limb Assessment (RULA) method under real working conditions.
Unlike studies conducted in controlled environments or with limited observation periods, this
research emphasizes continuous data collection and real-time posture assessment in dynamic,
industrial settings. By integrating objective sensor data with standardized RULA scoring, the study
aims to provide actionable insights and practical recommendations for ergonomic redesign and
intervention. This approach offers a scalable, cost-effective solution that industries can adopt to
monitor and improve workplace ergonomics. Furthermore, the research contributes to the growing
body of sensor-based ergonomic studies by demonstrating a real-world application model that can be
adapted across various sectors with similar physical demands.

6
 CHAPTER 3

IDENTIFIED RESEARCH GAPS

3.1. LIMITATIONS IN REAL-TIME ERGONOMIC RISK DETECTION

Existing literature highlights a significant shortfall in the ability of traditional ergonomic

assessments to capture real-time risk exposure. Most organizations rely on periodic audits,
observational checklists, or manual inspections, which are inherently limited in frequency and scope.
These methods often fail to detect subtle or transient postural deviations that occur during extended
work shifts. According to Stefana et al. (2021), such limitations can result in the accumulation of
biomechanical stress, ultimately contributing to musculoskeletal disorders (MSDs). This underscores
the need for continuous, high-resolution monitoring systems—such as wearable sensors—that can
provide objective, real-time data on posture and movement patterns.

3.2. CHALLENGES IN BEHAVIOUR-BASED SAFETY MONITORING

Behaviour-based safety (BBS) programs are widely recognized for promoting safe
work habits through observation and reinforcement. However, studies have noted that these
programs often suffer from inconsistencies in data collection, observer bias, and limited scalability.
Continuous monitoring of worker behaviour is rarely feasible due to resource constraints and privacy
concerns. Wearable technologies offer a promising alternative by automating the collection of
behavioral data, yet their adoption remains uneven across industries. Barriers such as cost,
infrastructure readiness, and user acceptance continue to limit widespread implementation, as noted
in recent reviews of wearable ergonomics systems.

3.3. DECLINING EFFECTIVENESS OF STANDALONE TRAINING PROGRAM

While ergonomic training has long been considered a key preventive strategy in workplace
safety programs, its standalone effectiveness is increasingly being challenged in contemporary
research. These programs typically aim to educate workers on proper lifting techniques, posture
maintenance, and workstation adjustments. However, numerous studies have found that the retention

7
of ergonomic knowledge and behaviors tends to decline significantly over time when training is not
consistently reinforced. In high-pressure environments such as assembly lines, warehouses, or
healthcare settings, workers often revert to unsafe practices due to time constraints, fatigue, or
operational demands—regardless of prior training.

The inability of traditional training programs to adapt to real-time workplace conditions has
prompted researchers to explore more dynamic and personalized solutions. Wearable technologies,
such as motion sensors and posture-monitoring devices, are emerging as promising tools to fill this
gap. These devices can provide continuous feedback to workers, alerting them in real time when
unsafe movements or postures are detected. They also generate data that can be used to tailor future
training interventions to the specific needs and behaviours of everyone.

3.4. INTEGRATION BARRIERS WITH EXISTING SAFETY INFRASTRUCTURE

A recurring challenge highlighted in the literature is the difficulty of integrating data

from wearable sensors into existing Environmental Health and Safety (EHS) systems. Many
organizations continue to rely on legacy platforms or maintain siloed data repositories that are not
equipped to handle real-time, sensor-based inputs.

This lack of interoperability limits the potential to perform comprehensive safety analyses or to
develop unified dashboards that present a complete picture of workplace risks. As a result, the value
of wearable technologies in ergonomic assessment is often underutilized. Furthermore, there has
been limited exploration of middleware solutions or API frameworks capable of bridging these
technological gaps. This reveals a significant shortfall in current research, both technically and
procedurally, in fully integrating sensor-driven insights into mainstream EHS infrastructures

3.5. NEED FOR ADVANCED DATA-DRIVEN DECISION SUPPORT

Despite the growing availability of ergonomic data from wearable devices, many
organizations lack the analytical tools to convert this information into actionable insights. Unlike
other domains of occupational safety that leverage predictive analytics, ergonomic programs often
rely on lagging indicators such as injury reports or absenteeism. The literature calls for the
development of robust data models and visualization tools that can support proactive decision-
making. This includes identifying high-risk zones, forecasting injury likelihood, and optimizing task
design based on real-time biomechanical data.

8
3.6 LIMITED APPLICATION IN REAL-WORLD INDUSTRIAL SETTINGS

While many studies on ergonomic risk assessment using sensors exist, a significant portion
are conducted in controlled laboratory or simulated environments. These controlled settings do not
fully capture the complexity and variability of real assembly line operations, where workers
encounter unpredictable tasks, varying workloads, and time pressures. This discrepancy limits the
practical applicability and reliability of existing findings when transferred to actual industrial
conditions.

3.7 SHORT DURATION OF DATA COLLECTION

A large number of ergonomic studies rely on short-term observations or data captured over
limited time frames. Such brief assessments fail to reflect the cumulative and long-term effects of
repetitive postures and movements experienced during a full work shift. Long-duration monitoring is
essential to identify intermittent or infrequent high-risk postures that may contribute significantly to
musculoskeletal disorders.

3.8 USE OF SPECIALIZED OR CUSTOM SENSOR SYSTEMS

Many research efforts use complex, expensive, and custom-built motion capture systems
requiring expert setup and operation. While these systems offer high precision, their cost and
complexity restrict their widespread adoption in everyday industrial settings. There is a gap in
research that explores the effectiveness of more affordable, commercially available sensor
technologies that are easier to deploy and maintain on the shop floor.

3.9 CHALLENGES IN ACCURATE INTEGRATION OF SENSOR DATA WITH ERGONOMIC TOOLS

Although integrating wearable sensor data with ergonomic assessment methods like RULA
has shown promise, the accuracy and consistency of automated scoring algorithms remain a
challenge. Current systems may not fully account for contextual factors such as task variability or
individual differences in movement patterns, leading to potential inaccuracies in ergonomic risk
evaluation. Standardization and validation of these integrated approaches are still ongoing areas of
research.

9
3.10 LACK OF CONTINUOUS REAL-TIME MONITORING AND FEEDBACK

Most existing ergonomic assessments provide periodic or post-hoc evaluations rather than
continuous monitoring. There is a research gap in developing systems that offer real-time feedback
to workers and supervisors, enabling immediate corrective actions to prevent injury. Continuous
monitoring systems could enhance early detection of risky postures and promote proactive
ergonomic interventions.

10
 CHAPTER 4

PROPOSED METHODOLOGICAL FRAMEWORK

The proposed methodological framework is designed to assess ergonomic risks in an

industrial assembly line setting using a combination of observational assessment and sensor-based
data collection. Central to this framework is the application of the Rapid Upper Limb Assessment
(RULA) method, a widely recognized tool for evaluating postural risks associated with
musculoskeletal disorders.

To enhance the objectivity and accuracy of this assessment, the study incorporates
a commercially available motion sensor worn by participants during normal working hours. This
sensor captures real-time kinematic data on upper limb, neck, and trunk movements. The framework
begins with a system design and requirements analysis phase, identifying specific ergonomic
challenges within the target environment and selecting appropriate sensors. Sensor calibration is
conducted to ensure accurate data capture under varying environmental conditions.

Collected sensor data is then synchronized with RULA scoring to assess risk levels
based on posture frequency, duration, and intensity. Sensor fusion and feature extraction techniques
are applied to filter noise and extract meaningful movement patterns, which are analyzed using
statistical or machine learning models to detect high-risk activities. The results are visualized through
ergonomic dashboards and used to generate targeted recommendations for workstation design, task
modification, or worker training. The framework also includes mechanisms for alerting and notifying
supervisors in cases of sustained high-risk postures and allows for integration into existing safety
systems. This end-to-end methodology ensures a scalable, real-world approach to ergonomic

assessment that is data-driven, repeatable, and adaptable across different industrial contexts .

11
 Study Design

Participants

Data Collection Tools

Data Collection Procedure

Data Processing and Analysis

Validation and Reliability

Ethical Considerations

Outcome and Recommendations

Limitations

Figure 4.1 Proposed Methodology

12
4.1 STUDY DESIGN

This study adopts a descriptive observational design to evaluate ergonomic risks in a

real-life industrial assembly line environment. Unlike experimental studies that manipulate variables
or control specific conditions, a descriptive observational approach focuses on capturing naturally
occurring behaviours and conditions without interference. Workers will be monitored as they
perform their routine duties, with no alterations to their workstation layout, workflow, or task
structure. This ensures that the collected data accurately reflects the physical demands, postural
habits, and ergonomic challenges inherent in the job.

The core rationale behind this design is to maintain ecological validity—that is, to study ergonomic
risks as they truly occur in practice rather than in an artificial or lab-based setup. It allows for a
holistic understanding of the interactions between workers, tools, and tasks in a dynamic production
environment. Real-time posture data will be collected using wearable motion sensors, which enables
continuous and objective monitoring throughout the work shift. By combining this with
observational notes and the application of the Rapid Upper Limb Assessment (RULA) method, the
study can systematically identify, classify, and evaluate postures that contribute to musculoskeletal
disorders.

The observational design is especially valuable in ergonomic research, as it helps uncover risk
factors that may not be evident in short-term or simulated assessments—such as posture repetition,
sustained awkward positions, and fatigue-related changes. In addition, the approach supports the
ethical requirement of minimizing disruption to workers and production flow while still gathering
meaningful data. The findings from this observational study will serve as a foundation for
developing targeted ergonomic interventions and informing policy recommendations aimed at
reducing injury risk and improving worker well-being in industrial settings.
4.2 PARTICIPANTS

The participants in this study will consist of assembly line operators engaged in repetitive tasks
that primarily involve upper limb movements. To ensure the reliability and relevance of the data
collected, specific selection criteria will be applied. First, participants must have a minimum of six
months of continuous experience in their current role. This requirement ensures that they are fully
familiar with their tasks and have established consistent postural patterns typical of the job, which is

13
4.1 STUDY DESIGN

This study adopts a descriptive observational design to evaluate ergonomic risks in a

real-life industrial assembly line environment. Unlike experimental studies that manipulate variables
or control specific conditions, a descriptive observational approach focuses on capturing naturally
occurring behaviours and conditions without interference. Workers will be monitored as they
perform their routine duties, with no alterations to their workstation layout, workflow, or task
structure. This ensures that the collected data accurately reflects the physical demands, postural
habits, and ergonomic challenges inherent in the job.

The core rationale behind this design is to maintain ecological validity—that is, to study ergonomic
risks as they truly occur in practice rather than in an artificial or lab-based setup. It allows for a
holistic understanding of the interactions between workers, tools, and tasks in a dynamic production
environment. Real-time posture data will be collected using wearable motion sensors, which enables
continuous and objective monitoring throughout the work shift. By combining this with
observational notes and the application of the Rapid Upper Limb Assessment (RULA) method, the
study can systematically identify, classify, and evaluate postures that contribute to musculoskeletal
disorders.

The observational design is especially valuable in ergonomic research, as it helps uncover risk
factors that may not be evident in short-term or simulated assessments—such as posture repetition,
sustained awkward positions, and fatigue-related changes. In addition, the approach supports the
ethical requirement of minimizing disruption to workers and production flow while still gathering
meaningful data. The findings from this observational study will serve as a foundation for
developing targeted ergonomic interventions and informing policy recommendations aimed at
reducing injury risk and improving worker well-being in industrial settings.
4.2 PARTICIPANTS

The participants in this study will consist of assembly line operators engaged in repetitive tasks
that primarily involve upper limb movements. To ensure the reliability and relevance of the data
collected, specific selection criteria will be applied. First, participants must have a minimum of six
months of continuous experience in their current role. This requirement ensures that they are fully
familiar with their tasks and have established consistent postural patterns typical of the job, which is

14
4.5 DATA PROCESSING AND ANALYSIS

The collected sensor data will first undergo preprocessing to ensure accuracy and
reliability. This involves cleaning the raw data to remove artifacts and noise using filtering
techniques such as low-pass filters. The continuous joint angle time series will then be segmented
based on specific tasks or predefined time intervals to facilitate focused analysis. A custom or
existing RULA scoring algorithm will be employed to translate the sensor-derived joint angles into
ergonomic risk scores. This algorithm maps the measured joint angles for the neck, trunk, upper
arms, forearms, and wrists into corresponding RULA posture scores. These individual scores will be
combined following the RULA methodology to produce an overall ergonomic risk score for each
analyzed time segment. Temporal analysis will be conducted to calculate time-weighted exposure,
determining how long workers maintain postures associated with varying risk levels. The frequency
and duration of high-risk postures will be examined to identify critical ergonomic hotspots.
Additionally, statistical analysis will include descriptive statistics such as mean, median, and range
of RULA scores across different workers and tasks. Comparative analysis will assess differences in
ergonomic risk exposure between various job roles or workstation designs, while correlation
analyses will explore potential relationships between risk levels and factors like task repetition rates
or worker demographics.
4.6 VALIDATION AND RELIABILITY

To ensure the accuracy and reliability of the ergonomic assessments, a subset of the
recorded sessions will undergo expert review. Experienced ergonomists will perform traditional
RULA evaluations based on video footage of the workers, and these expert-assessed scores will be
compared with the sensor-derived RULA scores to validate the measurement system. Prior to each
data collection session, sensors will be carefully calibrated to guarantee precise joint angle
measurements. Regular recalibration and quality control checks will also be conducted throughout
the study to maintain data integrity and minimize measurement drift. Additionally, reliability testing
will be performed by conducting test-retest measurements on selected participants during different
work shifts. This approach will help assess the consistency and repeatability of the sensor data and
RULA scoring, reinforcing the robustness of the methodology.

15
4.7 ETHICAL CONSIDERATIONS

The study will prioritize workers’ privacy and confidentiality throughout all stages of data
collection and analysis. All collected data will be anonymized to protect individual identities during
processing and reporting. Participation in the study will be entirely voluntary, with workers informed
of their right to withdraw at any time without any negative consequences. Additionally, strict safety
protocols will be implemented to ensure that the use of wearable sensors does not interfere with
normal work activities or pose any physical hazards to the participants. These ethical measures will
help maintain trust and compliance while safeguarding the wellbeing and rights of all involved.

4.8 OUTCOME AND RECOMMENDATIONS

The study aims to identify high-risk postures and specific tasks that contribute significantly to
ergonomic stress among assembly line workers. Based on these findings, targeted recommendations
will be made for ergonomic interventions, including workstation redesign, modification of tools, and
implementation of optimized work-rest schedules to reduce musculoskeletal strain. Additionally, the
project will propose the development of continuous monitoring protocols using wearable sensor
technology to facilitate ongoing assessment and management of ergonomic risks in the workplace. .

4.9 LIMITATIONS

One limitation of the study is the potential for sensor displacement during prolonged use,
which may impact the accuracy of the recorded data. This risk will be mitigated by securely
attaching sensors and performing periodic checks throughout the data collection process. Another
challenge is the possible influence of worker awareness of being monitored, known as the
Hawthorne effect, which may cause temporary changes in natural posture.

To reduce this bias, data will be collected over extended observation periods, allowing participants to
acclimate to the monitoring and exhibit their typical work behaviors. Which helps capture more
representative and natural postural data. Additionally, observational design does not control all
environmental or psychosocial factors that may influence posture, such as workload variability,
fatigue, or stress levels, which could impact ergonomic risk but are beyond the scope of this study.
Despite these challenges, the methodology incorporates multiple safeguards to maximize data quality
and ensure the findings remain valid and applicable to real-world assembly line settings.

16
 CHAPTER 5

SYNTHESIZED CONCLUSIONS FROM EXISTING RESEARCH

5.1 ERGONOMIC RISKS ARE PREVALENT IN ASSEMBLY LINE WORK

Extensive research highlights that assembly line workers commonly face significant
ergonomic challenges due to the nature of their tasks. The repetitive, forceful, and static postures
typical in assembly operations place considerable strain on the upper limbs, neck, and trunk. Studies
such as those by Punnett and Wegman (2004) have demonstrated the high prevalence of work-related
musculoskeletal disorders (WMSDs) in these populations. These disorders contribute to increased
absenteeism, decreased productivity, and substantial economic costs, underscoring the urgent need
for effective ergonomic risk management in assembly lines.

5.2 RULA: AN EFFECTIVE AND PRACTICAL TOOL FOR ERGONOMIC RISK SCREENING

The Rapid Upper Limb Assessment (RULA) tool, developed by McAtamney and Corlett
(1993), remains one of the most utilized methods for evaluating postural risks affecting the upper
body. Its scoring system, based on joint angles and force/load estimates, allows practitioners to
quickly identify workers at risk and prioritize ergonomic interventions. The simplicity and relative
ease of application make RULA particularly suitable for industrial environments. However, its
traditional use relies heavily on observational analysis, which can introduce subjective bias and may
overlook dynamic posture changes.

5.3 LIMITATIONS OF OBSERVATION-BASED ERGONOMIC ASSESSMENTS

While observational tools like RULA are widely accepted, their application is limited by
factors such as observer variability, time constraints, and snapshot sampling of work postures. These
constraints often lead to underestimation of risk exposure, particularly as they may fail to capture the
duration and frequency of high-risk postures over an entire work shift. Research by David (2005) and
others has highlighted the need for more objective and continuous ergonomic assessment methods to
overcome these limitations.

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5.4 WEARABLE SENSORS ENHANCE OBJECTIVITY AND CONTINUITY OF ASSESSMENT

The advent of wearable motion sensors, particularly Inertial Measurement Units (IMUs), has
revolutionized ergonomic assessment by enabling continuous, real-time capture of joint angles and
body movements. These sensors provide objective data that can reduce observer bias and capture the
dynamic nature of work postures across different tasks and time periods. Studies such as Yang et al.
(2020) and Huysamen et al. (2019) have demonstrated the successful application of sensor
technology to monitor ergonomic risks in industrial settings, confirming their reliability and
practicality.

5.5 INTEGRATION OF SENSOR DATA WITH RULA IMPROVES ASSESSMENT ACCURACY

Combining sensor-derived joint kinematics with the RULA method enhances the precision
and reliability of ergonomic risk evaluations. Automated scoring algorithms translate sensor data into
RULA scores, enabling continuous monitoring and immediate identification of high-risk postures.
This integration allows for more comprehensive ergonomic risk profiling, considering not only the
posture itself but also its duration and frequency. Research by Schall et al. (2018) supports the
efficacy of this hybrid approach, showing improved detection of ergonomic risks compared to
manual observation alone.

5.6 CHALLENGES AND GAPS IN REAL-WORLD APPLICATION

Despite promising advances, significant gaps remain in the widespread industrial adoption of
sensor-based RULA assessments. Much of the existing research has been conducted under controlled
laboratory conditions rather than in active manufacturing environments, limiting external validity.
Furthermore, many systems rely on expensive, custom-built sensors or require expert operation,
restricting accessibility for many industries. The need for validation studies using commercially
available, user-friendly sensor systems in real working conditions is critical. Additionally,
standardized protocols for sensor placement, data processing, and integration with ergonomic
assessment tools require further development.

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5.7 UNDERUTILIZATION OF CONTINUOUS MONITORING AND FEEDBACK MECHANISMS

Although continuous ergonomic monitoring using wearable sensors offers potential benefits for
injury prevention, it remains underexplored in industrial practice. Few studies have investigated how
real-time feedback can be provided to workers or supervisors to encourage timely posture correction
and reduce risk exposure. Research suggests that integrating sensor data with feedback systems could
enhance worker engagement and promote proactive ergonomic interventions, but practical
implementation strategies and effectiveness require further investigation.

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 REFERENCES

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