Gait-Based Parkinson’s Disease Detection Using Recurrent Neural Networks for Wearable Systems

Parkinson’s disease is one of the neurodegenerative conditions that has seen a significant increase in prevalence in recent decades. The lack of specific screening tests and notable disease biomarkers, combined with the strain on healthcare systems, leads to delayed detection of the disease, which worsens its progression. The development of diagnostic support tools can support early detection and facilitate timely intervention. The ability of Deep Learning algorithms to identify complex features from clinical data has proven to be a promising approach in various medical domains as support tools. In this study, we present an investigation of different architectures based on Gated Recurrent Neural Networks to assess their effectiveness in identifying subjects with Parkinson’s disease from gait records. Models with Long-Short term Memory (LSTM) and Gated Recurrent Unit (GRU) layers were evaluated. Performance results reach competitive effectiveness values with the current state-of-the-art accuracy (up to 93.75% (average ± SD: 86 ± 5%)), simplifying computational complexity, which represents an advance in the implementation of executable screening and diagnostic support tools in systems with few computational resources in wearable devices.