Classification of anomalous gait using Machine Learning techniques and embedded sensors
This provides a low-cost solution for economically vulnerable patients to detect gait pathologies, but it is incremental as it applies existing methods to a new dataset.
This work tackled the problem of detecting pathological gait disorders by developing an accessible wearable device with embedded sensors and machine learning techniques, achieving up to 96% accuracy in classifying four categories of anomalous gait.
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.