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Human gait can be a predictive factor for detecting pathologies that affecthuman locomotion according to studies. In addition, it is known that a highinvestment is demanded in order to raise a traditional clinical infrastructureable to provide human gait examinations, making them unaffordable foreconomically vulnerable patients. In face of this scenario, this work proposesan accessible and modern solution composed of a wearable device, to acquire3D-accelerometer and 3D-gyroscope measurements, and machine learning techniquesto classify between distinct categories of induced gait disorders. In order todevelop the proposed research, it was created a dataset with the target labelbeing 4 distinct and balanced categories of anomalous gait. The machinelearning techniques that achieved the best performances (in terms of accuracy)in this dataset were through the application of Principal Component Analysisalgorithm following of a Support Vector Machines classifier (94 \%). Further,an architecture based on a Feedforward Neural Network yielded even betterresults (96 \%). Finally, it is also presented computational performancecomparison between the models implemented.

Classification of anomalous gait using Machine Learning techniques and embedded sensors