Gait-Watch

Xu, Weitao; Shen, Yiran; Zhang, Yongtuo; Bergmann, Neil W.; Hu, Wen

doi:10.1145/3054977.3054991

Cited by 47 publications

(1 citation statement)

References 32 publications

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“…Beyond the typical step counting and fall detection applications, gait information can offer good indicators to recognize certain health conditions, such as Parkinson’s disease [12,13]. In the area of security, the 3D acceleration and angular velocity in someone’s gait can be used as a soft biometric to authenticate someone by the way the walk [14,15,16]. Indeed, accelerometers and gyroscopes can offer a wealth of information to make applications smart.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Comparison of Age and Gender Prediction on IMU Sensor-Based Gait Traces

hamme

Garofalo

Rúa

et al. 2019

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

A Systematic Comparison of Age and Gender Prediction on IMU Sensor-Based Gait Traces

hamme

Garofalo

Rúa

et al. 2019

Sensors

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Wearable Gait Authentication

Swathi,

Choudhary

et al. 2024

Optimized Predictive Models in Healthcare Using Machine Learning

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Continuous authentication using biometrics: An advanced review

Dahia

Jesus

Segundo

2020

WIREs Data Min & Knowl

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The shortcomings of conventional access control systems for high‐security environments have led to the concert of continuous authentication. Contrary to traditional verification, in which users are authenticated only once at the start of their session, continuous authentication systems regularly check users' identities to prevent hijackings. The challenges in this area involve balancing the security of protected assets by quickly detecting intruders with the system usability for genuine users. Biometric recognition plays a major role within this context, as it is the main way to assure that users are who they claim to be. A comparative analysis of the latest works revealed different aspects of this problem. First, some biometrics traits among those applied for continuous authentication are more suitable for this task than others. Second, systems combining multiple traits have advantages over those relying on a single one. Finally, many works fail to report proper evaluation metrics. With this in mind, we were able to identify new opportunities for researchers in the field. We highlight the potential for mining new datasets on the internet, which would benefit validation and benchmarking, and how recent deep learning techniques could address some of the open challenges in the area. This article is categorized under: Technologies > Prediction Technologies > Machine Learning Application Areas > Science and Technology

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Gait-Watch

Cited by 47 publications

References 32 publications

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

Wearable Gait Authentication

Continuous authentication using biometrics: An advanced review

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