The use of cameras to monitor the elderly daily living activities might cause inconvenience related to the privacy issues. Thus, another sensor namely LiDAR which generates point cloud data is used to support the monitoring process. This study is aimed at segmenting human and ground data from LiDAR point cloud to obtain human data. A segmentation process using a normal vector search approach for each point perpendicular to its plane using Principal Component Analysis (PCA) assisted by k-dimensional Tree Nearest Neighbor (kdTree-NN) Singular Value Decomposition (SVD) is proposed and successfully implemented. KITTI dataset containing of 54 frames in which a human is walking towards the LiDAR sensor as a scene scenario was used. The trend of the number of raw data points increased by 12.58%. Furthermore, the trend in the number of data points segmented representing human also increased by 233.13%. Meanwhile, the data points segmented representing ground decreased by 6.36%. This is because the closer human walking to LiDAR, the wider the blank spot behind the human object is. Consequently, data points representing human increased significantly, reducing the number of data points representing the ground. The point clouds which both represent the human and the ground were successfully segmented. Therefore, the point cloud representing human was successfully obtained to be used in further research.