Fall Detection System: LiDAR and Machine Learning for Non-Intrusive Monitoring

Abstract

Falls among older adults are a leading cause of injury and hospitalisation, yet existing fall detection devices, such as wearable sensors or video monitoring, encounter issues due to user compliance and privacy concerns. This paper presents a low-cost, privacy preserving fall detection system using a rotating LiDAR sensor mounted on a servo which is controlled by a simple microcontroller. A full sweep of a room captures approximately ten thousand depth points, which are streamed to a PC for pre-processing, feature extraction, and classification. Key geometric and distributional features are derived from point clouds and classified using a Random Forest model. Evaluation on fifty-three labelled scans achieved ninety-six-point two percent overall accuracy with only two misclassifications. These results demonstrate that affordable LiDAR combined with lightweight machine learning offers a viable path toward privacy-preserving, fall detection in care environments.