This study explores the use of machine learning to enhance patient mobility and caregiver ergonomics by optimizing 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.