Deep Learning Classification of Gait Disorders in Neurodegenerative Diseases Among Older Adults Using ResNet-50

Abstract

Gait disorders in older adults, particularly those associated with neurodegenerative diseases such as Parkinson’s Disease, Huntington’s Disease, and Amyotrophic Lateral Sclerosis, present significant diagnostic challenges. Since these NDDs primarily affect older adults, it is crucial to focus on this population to improve early detection and intervention. This study aimed to classify these gait disorders in individuals aged 50 and above using vertical ground reaction force (vGRF) data. A deep learning model was developed, employing Continuous Wavelet Transform (CWT) for feature extraction, with data augmentation techniques applied to enhance dataset variability and improve model performance. ResNet-50, a deep residual network, was utilized for classification. The model achieved a validation accuracy of 95.06% overall, with class-wise accuracies of 97.14% for ALS vs CO, 92.11% for HD vs CO, and 93.48% for PD vs CO. These findings underscore the potential of combining vGRF data with advanced deep-learning techniques, specifically ResNet-50, to classify gait disorders in older adults accurately, a demographic critically affected by these diseases.