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Updated: Aug 28, 2026
A Vibrotactile Feedback Device for Seated Balance Assessment and Training
Published on: January 20, 2019
Sensor-Based and AI-Driven Ergonomic Seated Posture Detection for Workplace Risk Prevention
Tatiana Teixeira1, Guilherme Barbosa1, Bruno Areias1
1INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal.
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.