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

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          Abstract

          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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          Most cited references 42

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          The effect of age on variability in gait.

           Seema Nayak,  A Gabell (1984)
          The intercycle variability in gait of two temporal parameters (stride time and double-support time) and the step-to-step variability of two spatial parameters (step length and stride width) were assessed in 64 healthy adults, 32 aged 21 to 47 and 32 aged 66 to 84. For all parameters the median values of the coefficient of variation did not differ significantly between the two groups. They were below 6% for step length and stride time but much higher for stride width and double-support time (between 17 and 27%). These differences in variability are discussed in relation to the control systems involved. Step length and stride time are thought to be determined predominantly by the gait-patterning mechanism, whereas stride width and double-support time may be determined predominantly by balance-control mechanisms. The results suggest that, in both age groups, the gait-patterning mechanisms are more consistent in their operation than are the balance-control mechanisms and that increased variability in gait should not be regarded as a normal concomitant of old age.
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            Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

            Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).
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              Eigenvoice modeling with sparse training data

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                04 July 2019
                July 2019
                : 19
                : 13
                Affiliations
                [1 ]imec-DistriNet, KU Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium
                [2 ]imec-COSIC, KU Leuven, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium
                Author notes
                [* ]Correspondence: davy.preuveneers@ 123456cs.kuleuven.be ; Tel.: +32-16-327-853
                Article
                sensors-19-02945
                10.3390/s19132945
                6651239
                31277389
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                Categories
                Article

                Biomedical engineering

                prediction, gait, age, gender, accelerometer

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