Classificação da marcha em parkinsonianos: análise dos algoritmos de aprendizagem supervisionada

Abstract

Parkinson’s disease is the second most prevalent neurodegenerative disease in the elderly, although its dominance and incidence vary according to age, gender and race/ethnicity. Studies indicate that the prevalence increases with age, with an estimate of 5 to 26 cases per 100,000 people per year, being approximately 1% among individuals aged 65- 69 and ranging from 3% to 14.3% among the elderly over 85 years. The most common clinical signs in the inflammatory process include the presence of resting tremor, muscle stiffness, bradykinesia and postural instability. The diagnosis of the disease is not a simple task, as it is known that there are stages patterns of disease progression in the human organism. However, many patients do not follow this progress because of the heterogeneity of manifestations that may arise. The gait analysis has become an attractive and non-invasive quantitative mechanism that can aid in the detection and monitoring of PD patients. Feature extraction is a very important task for quality of the data to be used by the algorithms, aiming as main objective the reduction in the dimensionality of the data in a classification process. From the reduction of dimensionality it is possible to identify which attributes are important and to facilitate the visualization of the data. For data related to human gait, the purpose is to detect relevant attributes that may help in identifying gait cycle phases, such as support and swing phases, cadence, stride length, velocity, etc. To do this, it is necessary to identify and select which attributes are most relevant, as well as the classification method. This work evaluates the performance of supervised learning algorithms in the classification of human gait characteristics in an open database, also identifies which attributes are most relevant to the performance of the classifiers in aiding the identification of gait characteristics in PD patients.