A Novel Vision-Based Fall Detection Scheme Using Keypoints of Human Skeleton with Long Short-Term Memory Network - Inturi, Anitha Rani; Manikandan, V. M.; Garrapally, Vignesh (Arabian journal for science and engineering (2011), 2023)
Human activity recognition plays a prominent role in applications like sports, violence detection, accident detection, women security, and smart homes by predicting abnormal human behaviour. Human activity recognition now offers a solution for fall detection systems that assist older people at home. A fall detection system that responds promptly to fatal falls can help to reduce the fall severity. Fall detection systems are developed using wearable environmental sensors and video data. Wearable devices are not always feasible as they cause inconvenience to the user. Hence, our paper presents a new promising solution for fall detection using vision-based approaches. In this approach, we analyse the human joint points which are the prime motion indicators. A set of key points of the subject are acquired by applying the AlphaPose pre-trained network. These keypoints are inferred to be the joint points of the subject. The acquired keypoints are processed through a framework of convolutional neural network (CNN) layers. Here, the spatial correlation of the keypoints is analysed. The long-term dependencies are then preserved with the help of long short-term memory (LSTM) architecture. Our system detects five types of falls and six types of daily living activities. We used the UP-FALL detection dataset for validating our fall detection system and achieved commendable results when compared to the state-of-the-art approaches. For comparison, we employed the OpenPose network for keypoint detection. It is inferred from the results that AlphaPose network is more precise in keypoint detection.