Musculoskeletal discomfort caused by prolonged sitting and unmonitored poor posture habits has become a

serious occupational health concern among university employees. Without any real-time corrective mechanism,

postural deviations accumulate silently into chronic back pain, neck strain, and shoulder tension — conditions

widely reported among faculty and staff at Pangasinan State University – Alaminos City Campus (PSU-ACC).

Survey results further confirmed that 95% of employees experience posture-related discomfort, yet none had

previously used a posture monitoring device, and 55% had no specific method to manage their sitting habits.

classifier with temporal validation. PosePal automatically detects improper sitting positions and instantly alerts

users to correct their posture, while providing posture scoring, analytics reports, personalized recommendations,

face recognition for secure identification, and an admin dashboard for institutional monitoring and management.

The system was developed following Agile methodology and evaluated by 21 respondents comprising faculty,

non-teaching staff, and an IT expert. Assessed against ISO/IEC 25010 quality standards, PosePal achieved an

overall weighted mean of 4.19 (Excellent), confirming its effectiveness, reliability, and usability in promoting

posture awareness and improving workplace wellness. The study concludes with recommendations to refine

posture detection and alert mechanisms, continuously evaluate system accuracy and user satisfaction, and

conduct further studies to expand system features and ensure PosePal remains adaptive and reliable across

diverse real-world settings.

upright positioning is also associated with increased alertness, improved focus, and positive emotional states,

while slouched or forward-leaning postures are linked to fatigue, stress, and symptoms of anxiety and depression

(Sharma & Rawat, 2023).

Despite these well-documented benefits, consistently maintaining proper posture in everyday settings presents a

significant challenge. Many individuals are unaware of the subtle postural deviations that occur during routine

tasks such as typing, reading, or using mobile devices. These micro-habits, when repeated over time, contribute

to cumulative strain and long-term musculoskeletal damage, particularly in academic and professional

environments where individuals maintain static positions for extended periods (Szczygiel et al., 2020). One

increasingly prevalent condition is Text Neck Syndrome (TNS), a repetitive stress injury resulting from sustained

head-down posture during handheld device use, now recognized as a public health concern linked to neck pain,

technologies has opened new possibilities for continuous, non-invasive posture assessment in workplace and

academic environments (Cheriyan et al., 2025). Incorrect sitting postures during extended computer use have

been consistently linked to muscular imbalances, spinal misalignment, backaches, and eye strain, reinforcing the

urgent need for automated monitoring solutions (Zaharuddin et al., 2025; Bassino et al., 2023). Improving the

accuracy and precision of posture detection through AI has been shown to empower office workers to adopt

healthier habits and significantly reduce the risk of developing musculoskeletal disorders (Bassino et al., 2023).

Vision-based posture monitoring systems are particularly effective because they are non-invasive, eliminate the

need for physical sensors, and preserve user comfort and privacy — making them highly suitable for institutional

deployment (Pistolesi et al., 2024; Ota et al., 2020). Furthermore, systems that provide real-time posture

feedback and clear corrective alerts are consistently perceived as more helpful and motivating, encouraging users

and none had previously used a posture monitoring device. Although 90% reported awareness of proper posture

practices, this knowledge had not translated into effective habit formation or prevention of physical strain.

To address these persistent concerns, this study developed PosePal, an AI-powered posture tracking and alert

system designed to monitor body alignment in real time using IP cameras and computer vision algorithms.

PosePal integrates AI-based posture analysis with user-centered design features, including personalized posture

analytics, corrective alerts, and cloud-based data management, to provide continuous ergonomic support in

academic workplace environments. The system was guided by the following objectives: (1) to assess the posture-

related challenges experienced by PSU-ACC employees and identify factors influencing poor posture habits; (2)

to identify the functional and non-functional requirements of the proposed system; (3) to identify appropriate

machine learning algorithms for system development; and (4) to determine the acceptability of the proposed

AI-based interventions through structured survey questionnaires, while the developmental component guided

the design, construction, and evaluation of the proposed system. Agile methodology was adopted throughout the

development process, enabling iterative refinement of system features based on continuous user feedback

collected across multiple sprint cycles. Purposive sampling was utilized to select 21 respondents comprising 12

faculty members, 8 non-teaching staff, and 1 IT expert — all of whom regularly engage in prolonged desk-based

computer work and were thus directly relevant to the study's objectives. While the sample size is acknowledged

as a limitation of this preliminary deployment, purposive sampling is considered appropriate in system

evaluation studies where respondents are selected based on direct relevance to the system's intended use context

(Kumar et al., 2025; Cheriyan et al., 2025). Similar AI-based posture monitoring studies conducted in

institutional settings have employed comparable respondent sizes during pilot evaluations, prioritizing

contextual relevance and domain familiarity over statistical breadth. System acceptability was evaluated using a

survey instrument adapted from the ISO/IEC 25010 software quality framework, with results interpreted through

The development of PosePal followed the Agile methodology, a modern software development approach that

emphasizes iterative progress, flexibility, and continuous user collaboration. As illustrated in Figure 1, the Agile

cycle consists of six phases — Plan, Design, Develop, Test, Deploy, and Review — each contributing to a

progressively refined and user-centered system. This iterative structure allowed the development team to release

functional components regularly, incorporate feedback after each sprint, and make swift adjustments to ensure

the final system effectively addressed the real ergonomic needs of PSU-ACC employees.

Source: https://asana.com/resources/agile-methodology

were deployed as the primary input devices, capturing live body alignment feeds for continuous AI-based

analysis through a Three-Tier Architecture comprising the Presentation, Application, and Data layers.

The testing phase involved both unit and system-level evaluations, with errors and inconsistencies identified and

resolved before deployment. PosePal was then deployed at PSU-ACC for pilot implementation, where users

received orientation and hands-on training on its features. The review phase gathered user feedback from faculty

and non-teaching staff, with observations used to refine alert mechanisms, interface design, and detection

accuracy. System acceptability was evaluated using a structured survey instrument based on ISO/IEC 25010

quality standards, with responses analyzed through weighted mean calculations using a five-point Likert scale

ranging from 1 (Poor) to 5 (Outstanding). Data gathered from this evaluation provided the empirical basis for

determining the system's overall performance, usability, and readiness for institutional adoption.

Employees of Pangasinan State University – Alaminos City Campus (PSU-ACC) are routinely exposed to

the identified posture problems, the system's requirements and features, the machine learning pipeline developed,

and the results of the acceptability evaluation conducted among PSU-ACC personnel.

Common Posture Issues and Contributing Factors Among PSU-ACC Employees

Employees of Pangasinan State University – Alaminos City Campus experience several posture-related

difficulties that affect their comfort, health, and overall work productivity. Common complaints include lower

back aches, shoulder tension, and neck stiffness — symptoms that result from improper sitting positions

sustained over prolonged work periods.

Faculty and non-teaching staff typically spend three to eight hours seated during academic and administrative

duties, a sedentary pattern that restricts physical movement, weakens core musculature, and limits blood

circulation. Compounding this is the lack of ergonomic furniture, as many offices still rely on traditional chairs

Awareness, Monitoring, Physical Condition, Work Routine, and Information. Each category highlights specific

issues — such as the absence of ergonomic furniture, lack of posture monitoring tools, prolonged sitting hours,

The core functional feature of the system—real-time posture monitoring with instant alert delivery—is illustrated

enables institutional-level monitoring and data management, supporting the campus-wide deployment of

PosePal.

PosePal employs a sophisticated multi-algorithm pipeline that integrates advanced computer vision and deep

learning techniques for real-time posture monitoring and biometric identification. Figure 8 illustrates the