Gait Parameter-based Deep Learning Models for Alzheimer’s Disease Classification

Abstract

Human gait refers to the walking patterns of individuals and abnormal gait may reveal the progression of various diseases. Here, we presented the gait parameter-based deep neural network for detecting the presence of Alzheimer’s disease. Initially, the raw gait data of sixty-nine participants were recorded using pressure sensors during a free walking test on the walkway system mat. The twelve spatiotemporal features and five ratio features of temporal gait parameters were then extracted. These features were used to construct the final sequential samples by concatenating every four consecutive strides, serving as the input for four classification models: Recurrent Neural Network, Long-Short Term Memory, Bidirectional Long-Shot Term Memory, and Gated Recurrent Unit. The best performance indicated the RNN model, which was shown in the F1-score of 0.909. This study demonstrated the feasibility of utilizing gait parameters-based Deep learning models as a wide-scale screening tool for Alzheimer’s disease, complementing the conventional cognitive screening instruments, while also accelerating the integration of Artificial Intelligence in global healthcare.