Fall Detection System for Elderly Activity Daily Living using Modified 2D LiDAR as a Low-Cost Alternative to 3D LiDAR based on the Slice Feature Approach

This study aims to propose and implement a Fall Detection System (FDS) for the Elderly Activity Daily Living (ADL) based on the modified two-dimensional (2D) Light Detection and Ranging (LiDAR) method, inspired by slice feature on three-dimensional (3D) LiDAR data. LiDAR-based FDS is an ambient-based FDS that utilizes infrared laser light. This work is an improvement from our previous proposed method, namely FDS based on 2D LiDAR data. Previous work presents a research gap analysis of falling and not falling classes based only on footprint-scanned data. Therefore, detecting other essential fall activities is impossible, such as falling forward, backward, sideward, and others. The main points carried out in this paper are the design and realization of modified 2D LiDAR, primary dataset collection, an accuracy test using K-Nearest Neighbors (K-NN), Random Forest (RF), as well as Support Vector Machine (SVM) algorithms. This work is comparable to the previous work that uses Infrared Array-based FDS. The test results in this paper prove that the modified 2D LiDAR has better performance than the previous study, especially in terms of sensitivity and selectivity performance parameters. The test results show that FDS achieves optimal performance using the RF algorithm with 98.89% sensitivity and 99.38% selectivity. This percentage value is superior to the previous study, with 98% sensitivity and 93% selectivity. In addition, the results show that the modified 2D LiDAR is feasible to be used as an ambientbased FDS solution.