Object Detection Human Activity Recognition for Improved Patient Mobility and Caregiver Ergonomics

This study explores the use of machine learning to enhance patient mobility and caregiver ergonomics by opti- mizing the use of mobility aids. Traditional manual assessments can be subjective and inaccurate, so this research develops a data-driven model for object detection and human activity recognition. A computer vision dataset was created using video recordings of controlled caregiving scenarios. The study leverages advanced machine learning models, including YOLO for object detection, pose estimation, ResNet-18 for frame classification, Inception-v4 for feature extraction, and LSTM for sequence modeling. The findings provide valuable insights into integrating machine learning into mobility aids, improving both patient outcomes and caregiver well-being.