Motor dysfunction is a critical, yet often underappreciated, component of neurodegenerative diseases such as Alzheimer's disease (AD) and related dementias. New research has shown that tasks involving fine motor behavior, visuospatial ability, and executive function in humans may have potential in early disease detection. However, little is known about the exact mechanisms by which these tasks predict cognitive and functional decline associated with AD. In the current method, a fine motor task, Kinematic Motor Ability Task (KINEMAT), was created to examine changes in motor ability across disease progression in preclinical models of AD relative to other neurodegenerative diseases and normal aging using free, open-source machine learning software. Here, proof-of-concept was established with the design, testing, and implementation of the fine motor task in a pilot study of Fisher 344 CDF rats with and without Harmaline-induced motor impairments. Additional validation, with an Alzheimer's disease rat model (TgF344-AD), further demonstrated model feasibility and task sensitivity. Rats were trained to reach through a small slot at the front of the chamber to retrieve a sugar pellet from one of three bowls with varying difficulty. Pellet retrieval was scored using one of three classifications: success (successfully grabbing and eating the sugar pellet), drop (grabs but drops the treat upon grasp), or failure (reach in which no pellet is procured, or pellets are knocked out of the bowl when attempting to grasp). Significant differences in motor ability were observed between control and Harmaline-treated rats, demonstrated by reduced total reaches and increased paw pose variability. TgF344-AD rats also showed reduced total reaches, as well as reduced successes across all bowls. In validating this as a fine motor task in rodents, it has future potential to study normal aging and preclinical models of neurodegenerative diseases, including AD, as pathology and symptoms emerge over time.