| Sumario: | Purpose: The primary goal of this study is to enhance safety and accessibility for individuals using wheelchairs by enabling automatic wheelchair detection through a visual surveillance system. This contributes to the development of smart healthcare systems that facilitate autonomous navigation and improve mobility support. Materials and Methods: A novel machine learning model based on the bag-of-visual-words (BoVWs) technique was developed for wheelchair detection. The approach involves key feature extraction, visual vocabulary construction, and histogram-based image representation. A support vector machine (SVM) classifier was employed to classify images based on these features after converting them into histograms of visual words. The model was evaluated using a publicly available image dataset. Results and Conclusions: The proposed method achieved an accuracy of 98.85%, demonstrating its effectiveness in identifying wheelchairs in images. These findings highlight the potential of object detection techniques in recognizing mobility aids, contributing to improved accessibility and safety in rehabilitation and assistive technology applications. IMPLICATIONS FOR REHABILITATION: This study investigates an automated method for identifying wheelchair users in images using the bag-of-visual-words (BoVWs) technique. Previous detection algorithms often struggle under adverse conditions such as low lighting, poor weather or occlusions. Development of automated method using computer vision technique to identify wheelchair users in images using classifiers has the potential to improve accessibility and the quality of life for individuals with disabilities. The proposed method categorises individuals as either wheelchair users or pedestrians. The results show improvements in the detection which can effectively identify mobility aids in images.
|
|---|