Joint Pedestrian Motion State and Device Pose Classification

Novel features for joint classification of gait and device modes are proposed and multiple machine learning methods are adopted to jointly classify the modes. The classification accuracy as well as the F
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score of two standard classification algorithms, K-nearest neighbor (KNN) and Gaussian process (GP), are evaluated and compared against a proposed neural network (NN)-based classifier. The proposed features are the correlation scores of a detected gait cycle relative to a set of unique gait signatures as well as the gait cycle time, all extracted from handheld inertial measurement units (IMUs). Each gait signature is defined such that it contains one full cycle of the human gait. In order to take the temporal correlation between classes into account, the initial classifiers' estimates are fed into a hidden Markov model (HMM) unit to obtain the final class estimates. The performance of the proposed method is evaluated on a large data set, including two classes of gait modes (walking and running) and four classes of device modes (fixed and face-up in the hand, swinging in the hand, in the pocket, and in the backpack). The experimental results validate the reliability of the considered features and effectiveness of the HMM unit. The initial classification accuracy of the NN-based approach is 91%, which is further improved to 99% after the smoothing stage on the validation set and 98% on the test set.