GeriatricCare 4.0: A Novel 3D Context-Based CareVision Framework for Fall Detection, Fall Classification and Fall Alerts for Elderlies
S. R. Patel, Amit Lathigara · Revue d intelligence artificielle · 2024
Falls can cause severe injuries, if an elderly person has a "long-life."In the presented research work, we have applied the widely known benchmark dataset "L2ei" and the customized "Geriatric-2000" dataset, which contains more than 6,000 elderly fall video sequences.The 25 human skeleton features are extracted using the customized 3D Open Pose methodology.The proposed research work presents a customized 3D Context-based LSTM-CNN enabled CareVision framework for fall position classification for elderlies.Furthermore, the proposed research work is compared with other customized AI-enabled computer vision approaches such as Fine KNN, Medium KNN, Decision Tree, long shortterm memory network (LSTM), Bi-LSTM and Recurrent Neural Network (RNN).The proposed 3D CareVision Framework has achieved an accuracy of 98.23 percent and a ROC value of 0.96.The indicated results demonstrate the efficiency and reliability of the proposed 3D CareVision Framework for elderly fall position classifications.The proposed 3D CareVision Framework will assist elderly personnels in case of emergencies and notify house members by sending emergency fall alerts.