A Systematic Comparison of Age and Gender Prediction on IMU Sensor-Based Gait Traces
A Systematic Comparison of Age and Gender Prediction on IMU Sensor-Based Gait Traces
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DOI：
10.3390/s19132945
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发表时间：
2019-07-01
期刊：
影响因子：
3.9
通讯作者：
Joosen, Wouter
中科院分区：
文献类型：
--
作者：
Van Hamme, Tim;Garofalo, Giuseppe;Joosen, Wouter
传感器通过测量和处理应用程序可以做出智能决策或通知其用户的信息，为许多智能应用程序和网络物理系统提供了基础。惯性测量单元（IMU）传感器，特别是加速度计和陀螺仪，在现代智能手机和可穿戴设备上随处可见。它们已被广泛应用于活动识别领域，跌倒检测和计步应用是该领域的突出示例。然而，这些传感器也可能会以开发人员不容易预见的方式偶然泄露敏感信息。更糟糕的是，敏感信息泄露给第三方，如推荐系统或定向广告应用程序，可能会导致不知情的最终用户的隐私问题。在本文中，我们探索了从IMU传感器获得的步态轨迹中提取年龄和性别信息，并系统地比较了不同的特征工程和机器学习算法，包括传统和深度学习方法。我们详细描述了我们的团队在第12届IAPR国际生物识别会议上的OU-ISIR基于可穿戴传感器的步态挑战：年龄和性别（GAG 2019）中使用的预测方法。在这两项比赛中，我们的团队在所有国际参赛者中获得了最佳解决方案，包括年龄和性别预测。我们的研究表明，在几秒钟的步态轨迹上以合理的准确度预测年龄和性别是可行的。此外，它说明了需要采取适当的措施，以减少滥用传感器作为敏感信息或私人特征的意外侧通道而造成的意外信息泄漏。
Sensors provide the foundation of many smart applications and cyber-physical systems by measuring and processing information upon which applications can make intelligent decisions or inform their users. Inertial measurement unit (IMU) sensors-and accelerometers and gyroscopes in particular-are readily available on contemporary smartphones and wearable devices. They have been widely adopted in the area of activity recognition, with fall detection and step counting applications being prominent examples in this field. However, these sensors may also incidentally reveal sensitive information in a way that is not easily envisioned upfront by developers. Far worse, the leakage of sensitive information to third parties, such as recommender systems or targeted advertising applications, may cause privacy concerns for unsuspecting end-users. In this paper, we explore the elicitation of age and gender information from gait traces obtained from IMU sensors, and systematically compare different feature engineering and machine learning algorithms, including both traditional and deep learning methods. We describe in detail the prediction methods that our team used in the OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender (GAG 2019) at the 12th IAPR International Conference on Biometrics. In these two competitions, our team obtained the best solutions amongst all international participants, and this for both the age and gender predictions. Our research shows that it is feasible to predict age and gender with a reasonable accuracy on gait traces of just a few seconds. Furthermore, it illustrates the need to put in place adequate measures in order to mitigate unintended information leakage by abusing sensors as an unanticipated side channel for sensitive information or private traits.