Object Detection Human Activity Recognition for Improved Patient Mobility and Caregiver Ergonomics
Year: 2025
Type: article
Abstract: 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.
Source: Journal of WSCG
Authors: Mahesh Madhavan, Patford Nkhoma, Reza Khoshkangini, Mahtab Jamali, Paul Davidsson, Jan Åberg, Arjo Sverige AB Malmö, Sweden 20120, Martin Ljungqvist, Axis Communications AB Lund, Sweden 22369
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412765459