Affiliation:
1. Southern University of Science and Technology, Shenzhen, Guangdong, China
Abstract
Falling is ranked highly among the threats in elderly healthcare, which promotes the development of automatic fall detection systems with extensive concern. With the fast development of the Internet of Things (IoT) and Artificial Intelligence (AI), camera vision-based solutions have drawn much attention for single-frame prediction and video understanding on fall detection in the elderly by using Convolutional Neural Network (CNN) and 3D-CNN, respectively. However, these methods hardly supervise the intermediate features with good accurate and efficient performance on edge devices, which makes the system difficult to be applied in practice. This work introduces a fast and lightweight video fall detection network based on a spatio-temporal joint-point model to overcome these hurdles. Instead of detecting fall motion by the traditional CNNs, we propose a Long Short-Term Memory (LSTM) model based on
time-series joint-point features
extracted from a
pose extractor
. We also introduce the increasingly mature RGB-D camera and propose 3D pose estimation network to further improve the accuracy of the system. We propose to apply tensor train decomposition on the model to reduce storage and computational consumption so the deployment on edge devices can to realized. Experiments are conducted to verify the proposed framework. For fall detection task, the proposed video fall detection framework achieves a high sensitivity of 98.46% on Multiple Cameras Fall, 100% on UR Fall, and 98.01% on NTU RGB-D 120. For pose estimation task, our 2D model attains 73.3 mAP in the COCO keypoint challenge, which outperforms the OpenPose by 8%. Our 3D model attains 78.6% mAP on NTU RGB-D dataset with 3.6× faster speed than OpenPose.
Funder
National Natural Science Foundation of China
Shenzhen Science and Technology Program
Innovative Team Program of Education Department of Guangdong Province
Publisher
Association for Computing Machinery (ACM)
Subject
Hardware and Architecture,Software
Reference43 articles.
1. Umar Asif, Benjamin Mashford, Stefan Von Cavallar, Shivanthan Yohanandan, Subhrajit Roy, Jianbin Tang, and Stefan Harrer. 2020. Privacy preserving human fall detection using video data. In Proceedings of the Machine Learning for Health Workshop. 39–51.
2. Multiple Cameras Fall Dataset;Auvinet Edouard;DIRO-Université de Montréal, Tech. Rep.,2010
3. A novel approach for fall detection in home environment
4. Fall detection based on the gravity vector using a wide-angle camera
5. Vision-Based Fall Detection With Multi-Task Hourglass Convolutional Auto-Encoder