A Systematic Review and Conceptual Framework for Smart Chair Technologies in Sitting Posture Monitoring

Abstract

Introduction: One of the reasons for musculoskeletal problems and deteriorating health is prolonged sitting in non-ergonomically structured positions. Objective: The current research aims to carefully examine the state-of-the-art of smart chair technologies for sitting posture monitoring and correction and to propose an integrated conceptual framework to guide future investigations. Method: A systematic literature review guided by the PRISMA framework was conducted on studies addressing sensor modalities, posture detection algorithms and ergonomic effects, and 20 studies were included in the final synthesis. Result and Discussion: The major findings were mostly shown that two main technologies, pressure and vision-based systems were utilized principally for the deep learning algorithms-CNNs categorization of postures. Newer ways have been offered to make users more adaptable and comfortable such as the use of non-invasive devices and transfer learning. However, there are substantial issues with the poor sensor resolution, lack of long-term validation and restricted system customisation. Conclusion: Thus, the multimodal framework calls for feedback systems that report to users, as well as adaptable basic sensors and sophisticated algorithms. This would enhance posture monitoring and encourage proper sitting habit in the long run.