Accelerometer-Based Human Identification for Multi-Walking States

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  • 複数の歩行状態に対応した加速度センサに基づく人物識別

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Nowadays mobile phones with some motion sensors like an accelerometer or a gyroscope are becoming widespread. Under this situation, there are various studies to identify humans or their activities through the data measured by the motion sensors. In this study we focus on identifying humans using data from an accelerometer. In many current studies that deal with human identification using an accelerometer, it is assumed that humans walk only on the flat floor. Here, we propose a two stage technique to identify not only humans but also their three walkingstates (‘walking on the flats’, ‘going up stairs’ and ‘going down stairs’). Specifically our method identifies the walking state at the first stage and subsequently identify humans based on the specific classifier for the respective walkingstates. We employed five classifiers (k-Nearest Neighbor, Classification And Regressive Trees, Na ve Bayes Classifier, Linear Discriminant Analysis and Support Vector Machine). From the results of the experiments with data from 10 human subjects, the best walking state identification of 95.7% accuracy was achieved by Linear Discriminant Analysis. Also, in case of human identification, the best performances obtained are: 85.0% accuracy for ‘walking on the flats’, 90.0% accuracy for ‘going up stairs’ and 77.0% accuracy ‘going down stairs’ with Linear Discriminant Analysis. The best human identification result with two stage process was obtained as 80.4%. It has also been found that for all the classifiers except k-Nearest Neighbor, walking states affect the identification result of a specific classifier and the two stage process (identifying walking state before identifying the human) of identification produce better result in terms of identification accuracy.

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