A Novel High Performance Object Identification Approach in Care Homes Using Gaussian Preprocessing
Sara Auweiler, Melina Mueller, Diana Puhla, Pascal Penava, Ricardo Buettner · IEEE Access · 2025
As the global population ages, the prevalence of dementia is expected to surge, necessitating innovative solutions to support the growing number of affected individuals. This research shows a innovative approach for the application of Convolutional Neural Networks for object identification in nursing home environments to enhance the independence and quality of life of dementia patients. Therefore we compare the performance of four well-known Convolutional Neural Network architectures — VGG16, VGG19, InceptionV3, and ResNet50 — to identify the most effective model for this application. Building on this, we use Gaussian blurring as an innovative preprocessing technique to improve model accuracy by reducing high-frequency noise and enhancing feature extraction. Our results indicate that ResNet50 outperforms other models, achieving the highest accuracy with 90.53%. Optimization through data augmentation and fine-tuning further enhances ResNet50’s performance to 94.69%. Applying the Gaussian filter significantly improves the model’s accuracy to 96.81%, demonstrating its potential as a crucial preprocessing step and setting a new benchmark for this application. Our findings provide valuable insight into applying the Gaussian filter on Convolutional Neural Networks and pave the way for developing intelligent support systems to assist dementia patients in their daily tasks and improve their overall well-being.