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

Sensors. 2019;19(13):2945 DOI 10.3390/s19132945


Journal Homepage

Journal Title: Sensors

ISSN: 1424-8220 (Online)

Publisher: MDPI AG

LCC Subject Category: Technology: Chemical technology

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML



Tim Van hamme (imec-DistriNet, KU Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium)

Giuseppe Garofalo (imec-DistriNet, KU Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium)

Enrique Argones Rúa (imec-COSIC, KU Leuven, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium)

Davy Preuveneers (imec-DistriNet, KU Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium)

Wouter Joosen (imec-DistriNet, KU Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium)


Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks


Abstract | Full Text

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.