Aug 2026· Italian National Conference on Sensors· Vol 26, pp. 5120· 0 citations· 40 references
Medicine
TL;DR
A chair designed for office monitoring capable of accurately identifying ten representative seated postures and measuring environmental factors was developed and validated and presented an initial promising first step for an ergonomic office management solution.
Abstract
Background: Work-related musculoskeletal disorders (WMSDs) remain one of the most prevalent occupational health problems worldwide. To prevent the development of these WMSDs in an office space, a chair designed for office monitoring capable of accurately identifying ten representative seated postures and measuring environmental factors was developed and validated. Methods: To evaluate office working conditions, the chair has three embedded Printed Circuit Boards (PCBs): one directed towards seat pressure management, one directed towards environmental measurements and one PCB to manage the entire system. Machine learning approaches were then applied to establish a model that effectively predicts the seated position. The environmental data were also analyzed. Results: The seated position classification presented an accuracy of 80.99% in controlled conditions, while in a real-world context the accuracy was 65.98%. The environmental management showed low errors, except for the PM2.5 and PM10, with relative errors above 30%. Conclusions: This work presents an initial promising first step for an ergonomic office management solution.
Work-related musculoskeletal disorders (WMSDs) constitute the most prevalent occupational injury in manual material handling (MMH) across Europe. WMSDs lead to significant socio-economic losses. Multiple observational methods have related working conditions and ergonomics in MMH and workplaces to the risk of developing WMSDs for targeted and informed risk mitigation strategies. Digital ergonomic evaluations represent the next step in this process, complementing observational methods with statistical analysis and monitoring the evolution of risks over time. IMU-based motion capture technologies play a key role in this perspective due to their ease of integration into worker suits. However, IMU sensors are prone to estimation errors that affect the user kinematic and the task reconstruction. Optimization techniques, human activity recognition algorithm, and the posture-specific spatial calibration (PSSC) can refine the accuracy of whole-body kinematic models, enabling precise ergonomic assessments in real-time. This pilot study analyzes a standard MMH activity. Applying the proposed PSSC, the user kinematics is reconstructed with an average error of 50 mm. The optimized model improves the ergonomic risk evaluation by 60% w.r.t. the non-optimized one. Compared to the observational method, the lifting index of the digital ergonomic evaluation differs by 0.05, representing the 1.6% of the full-scale.
S. Leggieri, D. Caldwell, Christian Di Natali· International Conference on...· 0 citations
Improper seated work postures are common in the workplace and can lead to various musculoskeletal problems, ranging from joint pain to permanent disability. Continuous assessment of seated work postures is necessary to help prevent these risks. However, existing studies have often relied on rule-based methods, which are highly vulnerable to measurement noise from motion capture devices. To overcome this limitation, this study proposes a learning-based approach for posture assessment. Ten participants were recruited to mimic the seven most common awkward and potentially risky seated work postures, while joint angles of their upper body were recorded and computed using both an RGB video-based approach and a Vicon motion capture system-based approach. The RGB measurements were used as features, while the Vicon measurements were used to derive accurate reference labels by comparison against Rapid Upper Limb Assessment (RULA) criteria. A multi-output Random Forest classifier was trained to predict joint-level posture assessment scores, and the model performance was evaluated using a leave-one-subject-out cross-validation scheme. The results provide initial evidence that the model achieved high performance in neck score assessment, while the trunk, shoulder, and elbow scores were more sensitive to posture types and inter-subject differences in posture execution. A larger dataset with more posture types or more subjects would improve the robustness and generalizability of the model.
Xiaoyi Wang, C. Or· AHFE International· 0 citations
Maintaining proper lumbar posture is important for preventing musculoskeletal disorders among sedentary individuals, yet conventional interventions such as exercise and physical therapy are often impractical for sustained daily use, and existing wearable posture aids remain limited in sensor integration, adaptability, and real-time intelligence. This study developed and evaluated PosTer, a smart lumbar posture correction vest that combines Inertial Measurement Unit (IMU) and flex sensors with an ESP32-based web interface and a Fully Connected Neural Network (FCNN) for real-time posture classification, providing visual, auditory, and automatic corrective feedback. A quantitative developmental design was used; the system was tested by 126 purposively selected office-based employees of a Business Process Outsourcing (BPO) company in Calamba City, Philippines, representing individuals in low-intensity, sedentary work. Sensor readings and an ISO/IEC 25010-based survey were used to evaluate classification performance and system quality. The FCNN achieved 99.43% accuracy with precision, recall, and F1-score of 0.985, outperforming Logistic Regression (93.40% accuracy) and Support Vector Machine (99.33% accuracy). ISO/IEC 25010 evaluation showed strong mean ratings for functional suitability (3.84), performance efficiency (3.91), usability (3.83), reliability (3.68), and safety (3.80). Participants reported that the web interface was clear and that the vest responded promptly to posture deviations. These findings indicate that PosTer is an effective assistive tool for real-time lumbar posture monitoring and correction in short-term, non-clinical, sedentary settings, although its restricted vest sizing and dependence on correct sensor placement limit its suitability for long-term or clinical therapeutic use. The study provides a foundation for further work on sensor adaptability, extended-use evaluation, and mobile-based posture analytics.
Lizette Torrecampo, Ashley Ferrer, Ryan James Velasquez et al.· Asian Research Journal of In...· 0 citations
Workplaces in construction and industry suffer from a significant number of workplace accidents because of a lack of safety mechanisms like helmets and high-visibility vests. In order to solve this issue, an automatic detection system for detecting the presence of the worker's helmet & safety vest using a deep learning model is developed. You Only Look Once – Neural Architecture Search (YOLO-NAS) algorithm was chosen to be used in the model because of its fast and high-quality detection process. A dataset with images containing healthy workers wearing helmets & safety vests (with some workers appearing in different safety vests) is collected and used as the training set. The resulting model is then saved for future usage in making predictions with new images. Then, this model is connected to Streamlit, which provides a convenient Web-based user interface through which a user can provide an image as an input to the model. The trained neural network is applied to the input image, and all detected objects that correspond to helmet & safety vest are put in bounding boxes on the input image. Moreover, each of the bounding boxes is labeled with the name of the object and confidence score of the detection. Thus, a clear visualization of the safety equipment worn by the worker is provided. Overall, the developed detection system significantly decreases the human intervention needed for visual verification of the safety helmet or vest. The described system is a user-friendly and cost-effective way of evaluating workplace safety compliance.
S. Vijayakumar, Loganathan Nachimuthu, Balasubramaniam C et al.· 2026 International Conferenc...· 0 citations
Prolonged sitting and poor sitting posture have become significant occupational health concerns among office workers, contributing to musculoskeletal disorders, fatigue, and reduced work productivity. This study proposes SITCARE, an Internet of Things (IoT) based office sitting behavior monitoring system that integrates an FSR402 pressure sensor, an MPU6050 gyroscope, and an HC-SR04 ultrasonic sensor using a sensor fusion approach. The proposed system continuously monitors sitting duration, chair inclination, and seat height, while transmitting real time sensor data to a Firebase Realtime Database for web-based visualization. In addition, an automated Telegram chatbot provides reminders to encourage regular breaks and adequate hydration. The developed system was evaluated through sensor validation, communication performance testing, system functionality assessment, and user acceptance evaluation. Experimental results demonstrated reliable sensor operation, stable cloud communication with an average round trip latency of 220 ± 50 ms and zero packet loss, as well as successful real time dashboard visualization and notification delivery. Furthermore, user evaluation involving 20 participants achieved an overall satisfaction score of 4.50 out of 5, indicating positive acceptance of the proposed system. These findings demonstrate that SITCARE provides an effective and practical IoT based solution for promoting ergonomic awareness and healthier workplace behavior among office workers.
Kevin Sutanto, Putri Caezylia Rundupadang, Louie Jason Liman et al.· Jurnal Informatika dan Tekni...· 0 citations
The objective of this study was to assess the performance of posture recognition systems based on artificial intelligence (AI) using deep learning (DL) and machine learning (ML) approaches for the prevention of work-related musculoskeletal disorders (WMSDs) in manufacturing. The study was conducted as a systematic review and meta-analysis following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four open-access databases were screened in May 2026 without date restrictions: PubMed/MedLine, Google Scholar, ScienceDirect, and IEEE Xplore. The selected studies had to be original, peer-reviewed studies written in English. The study had to evaluate the performance of an AI posture recognition system (ML or DL) for the prevention of WMSDs in manufacturing using at least one of the following parameters: accuracy, specificity, sensitivity, precision, or F1 score. The risk of bias for each included study was assessed using PROBAST (Prediction Model Study Risk of Bias Assessment Tool). A meta-analysis was conducted to pool the values of the five performance metrics separately. Logit and Freeman-Tukey transformations were applied prior to pooling, and the results were compared after back-transformation. Cochran’s Q test, the I2 statistic, and inter-study variability (τ2), computed using the generalized inverse variance method with the restricted maximum likelihood model, were applied to assess heterogeneity. Forest plots including pooled values with 95% confidence intervals were used to present the results. Subgroup analyses and meta-regressions were performed to test the effect of AI methods and ergonomic tools on performance and to explore potential causes of heterogeneity. Finally, publication bias (Egger’s test) and certainty of evidence (GRADE method—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to ensure the generalizability of the results. Ten studies were included: Among the 200 studies identified through database searches and the snowball method, 12 met the inclusion criteria and were selected. Two studies were excluded due to an insufficient number of participants, bringing the final number of studies considered to 10. The logit transformation yielded the best overall fit for the normality of the data distribution for the use of a random-effects model. High posture detection performance was observed, with pooled values ranging from 84.78% (95% CI: 80.23–88.19%, specificity with Freeman-Tukey) to 93.40% (95% CI: 89.57–95.89%, precision with logit). The values obtained with logit were higher than those obtained with Freeman-Tukey, with differences ranging from 2.75% to 4.75% across all performance metrics. Meta-regression showed that DL outperformed ML for all metrics, with differences ranging from 5% to 17%. RULA and REBA achieved better performance than other ergonomic tools. However, high heterogeneity (I2 > 90%) and substantial inter-study variability were observed in all analyses, and a very low level of evidence was evidenced for all performance parameters. Consequently, the results should be interpreted with caution, particularly regarding the deployment of the systems in real-world settings. Future work could strengthen training and testing procedures on datasets, as well as external validation. These aspects are essential for effective use in manufacturing environments to ensure operator safety by reducing their exposure to WMSD risks associated with work postures.
J. Jacquier-Bret, P. Gorce· Theoretical and Applied Ergo...· 0 citations