Huntington disease (HD) is a progressive disorder of motor, cognitive, and psychiatric disturbances. A general lack of coordination and an unsteady gait often follow motor speed, fine motor control, and gait are affected. Gait disturbance is one of the main factors contributing to a negative impact on quality of life of patients. The state-of-the-art of assessment approaches for the evaluation and recognition of this type of disease are expensive ambulation-based performed under the supervision of clinicians. Our research aim at overcoming these issues by defining an in-house self-test mobile solution able to detect anomalies in the gait dynamics of elderly. In this paper, we present the preliminary results of our research exploring a deep learning-based model for the automatic assessment of the gaits dynamics of elderly people. The gait dynamics signal is measured by means of a temporal time series of the acceleration values of the patient's acceleration movements along the (x,y,z) axes. Our experiments show classification results reaching a good accuracy rate at 0.75% with a recall an precision rate at 0.70% and 0.75%.
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