Visualization of Caregiving Posture and Risk Evaluation of Discomfort and Injury

Abstract

There is a high risk of musculoskeletal discomfort and injury due to the lack of professional guidance and training in caregiving postures. This study aimed to develop a risk assessment and visualization method by analyzing caregiving postures. Participants with (n = 8) and without (n = 10) caregiving experience were recruited to simulate patient transfer from bed to wheelchair. The Rapid Entire Body Assessment (REBA) method lacked sensitivity in distinguishing the experienced and inexperienced groups. We found that the visualization of the center of gravity (COG) trajectory could represent distinct posture differences between the two groups. Based on this finding, we considered a modified REBA method combining the COG trajectory, load-bearing time, and asymmetric load parameters, named the Caregiving-REBA (C-REBA) method. Our results demonstrated that C-REBA could effectively distinguish experienced and inexperienced caregivers, especially in caregiving task Stages 2–4. In conclusion, the present work explored adjusting to the parameters of the REBA method. The proposed C-REBA method could be easily imbedded into the Internet of Things (IoT) device to assess the caregiving posture for providing visual guidance and warning of the risk of discomfort or injury.

Compensation Method for Missing and Misidentified Skeletons in Nursing Care Action Assessment by Improving Spatial Temporal Graph Convolutional Networks

“…Moreover, the use of sensors may impede the normal work of healthcare personnel [12,13]. In contrast, vision-based posture motion capture methods offer a non-contact approach that does not disrupt the tasks of healthcare providers [14]. Currently, this approach primarily relies on machine learning algorithms to recognize motion pose keypoints from images or videos [15,16], enabling the automatic calculation of the REBA posture score using these keypoints.…”