Sensor-Based and AI-Driven Ergonomic Seated Posture Detection for Workplace Risk Prevention
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.