A LiDAR-Based Recommendation System to Improve Sitting Posture Respecting Privacy.

Abstract

Poor posture is becoming more widespread due to the rising number of jobs that require workers to sit for extended hours. Maintaining proper leg positioning is essential for good overall posture and long-term health. However, current monitoring methods involve multiple sensors and cameras, leading to discomfort and privacy concerns. This paper presents a privacy-preserving recommendation system using Light Detection and Ranging (LiDAR) to monitor leg positions. The system captures the horizontal leg outline at knee height and extracts domain-specific features, while a stacked autoencoder reproduces the latent representation of the leg outline. The system comprises three modules, including one-class and multi-class support vector machines, to identify 15 leg positions and reject unrecognized ones. Data from 30 healthy volunteers during work activities trained the system. When tested on new participants, the system achieved an accuracy of over 98%. In addition to monitoring leg positions while respecting privacy and ergonomics, our system alerts workers about poor leg positions and generates intuitive dashboards with posture statistics that help safety engineers identify at-risk workers and body parts. This offers a promising solution for improving desk workers' posture and reducing long-term health issues, respecting privacy.