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Estimation of Ground Contacts from Human Gait by a Wearable Inertial Measurement Unit using machine learning

Robotics system for rehabilitation of movement disorders and motion assistance are gaining increased intention. In this scenario estimation of ground contact is an active area of research in robotics and healthcare. This article addresses the estimation and classification of right and left foot during the healthy human gait based on the IMU sensor data of chest and lower back. For this purpose we have collected an IMU data of 48 subjects by using two smartphones at chest and lower back of the human body and one smart watch at right ankle of the body. To show the robustness of our approach data was collected at six different surfaces (road tiles carpet grass concrete and soil). The recorded data of lower back and chest sensor was segmented into single steps on the basis of right ankle sensor data, then we computed a total of 408 features from time frequency and wavelet domain of each segmented step. For classification task we have trained two machine learning classifiers SVM and RF with 10 fold cross validation method. We performed classification experiments at individual surfaces, hard surfaces, soft surfaces and all surfaces, highest accuracy was achieved at individual surfaces with accuracy index of 98.88%. Furthermore, classification rate at hard soft and all surface are 95.60%, 94.38% and 95.05% respectively. The results shows that estimation of ground contact form normal human walk at different surfaces can be performed with high accuracy using 6D data of angular velocities and accelerations from chest and lower back location of the body.

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