The increasing prevalence of neurodegenerative diseases (NDDs) impose substantial medical and public health burdens on populations throughout the world. NDDs are chronic diseases that affect the human central nervous system causing loss of neurons within the brain and/or spinal cord. This causes deterioration in movement and mental functioning of the patients. The current medications for this group of disorders are limited and aim to treat the symptoms only. A better understanding of the mechanisms underlying neurodegeneration should lead to more effective, disease-modifying treatments in the future. Continuous assessment of NDD patients is a key element of future care and treatment. This contribution proposes a wearable NDD detection system based on patient’s gait dynamics using an unobtrusive force resistive sensor embedded in patient’s shoe. The NDD classification is based on 3 fundamental gait features: stride time, stride time’s fluctuation and the autocorrelation decay factor. It is designed to discriminate between healthy subjects and NDD patients and moreover identify the NDD type: (Huntington’s disease (HD), Parkinson Disease (PD), and Amyotrophic Lateral Sclerosis (ALS)). The proposed NDD classification algorithm is implemented on FPGA and verified experimentally using Gait Dynamics dataset from Physionet. It offers a classification accuracy of 93.8%, 89.1%, 94% and 93.3%, for ALS, HD, PD, and healthy person, respectively, from a total set of 64 subjects.