A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders

The change from face-to-face work to teleworking caused by the pandemic has induced multiple workers to spend more time than usual in front of a computer; in addition, the sudden installation of workstations in homes means that not all of them meet the necessary characteristics for the worker to be able to position himself/herself comfortably with the correct posture in front of their computer. Furthermore, from the point of view of the medical personnel in charge of occupational risk prevention, an automated tool able to quantify the degree of incorrectness of a postural habit in a worker is needed. For this purpose, in this work, a system based on the postural detection of the worker is designed, implemented and tested, using a specialized hardware system that processes video in real time through convolutional neural networks. This system is capable of detecting the posture of the neck, shoulders and arms, providing recommendations to the worker in order to prevent possible health problems, due to poor posture. The results of the proposed system show that this video processing can be carried out in real time (up to 25 processed frames/sec) with a low power consumption (less than 10 watts) using specialized hardware, obtaining an accuracy of over 80% in terms of the pattern detected.

Automated summary

The shift to teleworking during the pandemic has resulted in increased computer usage, with some workstations not meeting the necessary requirements for comfortable and correct posture. Medical personnel are in need of an automated tool to quantify incorrect posture habits. A system based on posture detection using convolutional neural networks was developed and tested, showing a high accuracy rate of over 80% with real-time video processing.