Posture Monitoring for Health Care of Bedridden Elderly Patients Using 3D Human Skeleton Analysis via Machine Learning Approach

Abstract

For bedridden elderly people, pressure ulcer is the most common and serious complication and could be prevented by regular repositioning. However, due to a shortage of long-term care workers, repositioning might not be implemented as often as required. Posture monitoring by using modern health/medical caring technology can potentially solve this problem. We propose a RGB-D camera system to recognize the posture of the bedridden elderly patients based on the analysis of 3D human skeleton which consists of articulated joints. Since practically most bedridden patients were covered with a blanket, only four 3D joints were used in our system. After the recognition of the posture, a warning message will be sent to the caregiver for assistance if the patient stays in the same posture for more than a predetermined period (e.g., two hours). Experimental results indicate that our proposed method is capable of achieving a high accuracy in posture recognition (above 95%). To the best of our knowledge, this application of using human skeleton analysis for patient care is novel. The proposed scheme is promising for clinical applications and will undertake an intensive test in health care facilities in the near future after redesigning a proper RGB-D (Red-Green-Blue-Depth) camera system. In addition, a desktop computer can be used for multi-point monitoring to reduce cost, since real-time processing is not required in this application.