A Fall Risk Prediction System Based on 3D Space Human Skeleton Torso Images
- Resource Type
- Conference
- Authors
- Chang, Wan-Jung; Chen, Liang-Bi; Su, Jian-Ping; Chen, Ming-Che; Yang, Tzu-Chin
- Source
- 2021 IEEE International Conference on Consumer Electronics (ICCE) Consumer Electronics (ICCE), 2021 IEEE International Conference on. :1-2 Jan, 2021
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Transportation
Torso
Three-dimensional displays
Hospitals
Hidden Markov models
Predictive models
Markov processes
Skeleton
Artificial intelligence (AI)
deep learning
fall risk prediction
rehabilitation
Hidden Markov model (HMM)
- Language
- ISSN
- 2158-4001
This paper proposes a fall risk prediction system, which adopts two cameras and an AI server to obtain the images of the 3D human skeleton torso activities. We use the Hidden Markov model (HMM) for the movement changes of the joint points of the human skeleton torso in 3D space to evaluate the probability of falling of subjects. The experimental results show that the proposed system can efficiently predict the risk of falling for consumers with unbalanced gait.