  Human gait can be a predictive factor for detecting pathologies that affect
human locomotion according to studies. In addition, it is known that a high
investment is demanded in order to raise a traditional clinical infrastructure
able to provide human gait examinations, making them unaffordable for
economically vulnerable patients. In face of this scenario, this work proposes
an accessible and modern solution composed of a wearable device, to acquire
3D-accelerometer and 3D-gyroscope measurements, and machine learning techniques
to classify between distinct categories of induced gait disorders. In order to
develop the proposed research, it was created a dataset with the target label
being 4 distinct and balanced categories of anomalous gait. The machine
learning techniques that achieved the best performances (in terms of accuracy)
in this dataset were through the application of Principal Component Analysis
algorithm following of a Support Vector Machines classifier (94 \%). Further,
an architecture based on a Feedforward Neural Network yielded even better
results (96 \%). Finally, it is also presented computational performance
comparison between the models implemented.
