Gender recognition using motion data from multiple smart devices
Gender recognition using motion data from multiple smart devices
复制标题
使用来自多个智能设备的运动数据进行性别识别
DOI：
10.1016/j.eswa.2020.113195
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发表时间：
2020-06-01
影响因子：
8.5
通讯作者：
Cai, Zhongmin
中科院分区：
文献类型：
--
作者：
Dong, Jianmin;Du, Youtian;Cai, Zhongmin
随着可穿戴设备的普及，同时使用智能手机和智能手表等多个智能设备正在成为一种流行的生活方式。这种多传感器设置为通过多个数据融合进行增强的用户特质分析提供了新的机会。在这项研究中，我们通过使用从多个智能设备收集的运动数据来探索性别识别任务。具体地，同时从智能手机和智能带收集运动数据。从采集到的运动数据中提取运动特征，从时域、频域和小波域三个方面进行分析。我们提出了一种考虑运动特征之间冗余的特征选择方法。性别识别使用四种监督学习方法进行。实验结果表明，使用从多个智能设备收集的运动数据可以显着提高性别识别的准确性。在56名受试者的数据集上对我们的方法进行的评估表明，与单独使用智能手机和智能手环时的93.7%和88.2%的准确率相比，它可以达到98.7%的准确率。(C)2020爱思唯尔有限公司保留所有权利。
Using multiple smart devices, such as smartphone and smartwatch simultaneously, is becoming a popular life style with the popularity of wearables. This multiple-sensor setting provides new opportunities for enhanced user trait analysis via multiple data fusion. In this study, we explore the task of gender recognition by using motion data collected from multiple smart devices. Specifically, motion data are collected from smartphone and smart band simultaneously. Motion features are extracted from the collected motion data according to three aspects: time, frequency, and wavelet domains. We present a feature selection method considering the redundancies between motion features. Gender recognition is performed using four supervised learning methods. Experimental results demonstrate that using motion data collected from multiple smart devices can significantly improve the accuracy of gender recognition. Evaluation of our method on a dataset of 56 subjects shows that it can reach an accuracy of 98.7% compared with the accuracies of 93.7% and 88.2% when using smartphone and smart band individually. (C) 2020 Elsevier Ltd. All rights reserved.