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학술대회 Random Forest Based-Biometric Identification Using Smart Shoes
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김정균, 이강복, 홍상기
International Conference on Sensing Technology (ICST) 2017, pp.218-221
This study presents a biometrie identification based on gait (with shoe wearable sensors). Biometrie identification is an excellent method to often alternate inconvenient interaction such as PIN and patterns in smart device. To help elderly person who cannot control smart devices by themselves, it is required to assist automatic personalization by identifying users sharing a device. In this study, we proposed an algorithm combined the discrete cosine transform for detecting frequency feature and random forest which classifies subjects. We performed an experiment for 8 subjects by walking with the smart shoes. Finally, the result demonstrates a user recognition accuracy of 97.9 % and an equal error rate of 2.4%.
KSP 제안 키워드
Biometric Identification, Discrete cosine Transform, Elderly person, Random forest, Recognition Accuracy, Smart devices, User recognition, Wearable sensors, automatic personalization, equal error rate, frequency feature