Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Demographics <s> The gait patterns of twelve women were compared during walking in flat shoes and in shoes with high heels. In addition, four anthropometric measurements were recorded. Results showed that the wearing of high heels caused a significant decrease in step length and stride length, whereas there was a minimum change in the stride width and foot angle <s> BIB001 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Demographics <s> BACKGROUND ::: More information about the physical activity of adolescents is needed. This study used objective measurement to investigate differences in activity patterns related to gender, body mass index (BMI), day, and time of day. ::: ::: ::: METHODS ::: Eighth-grade adolescents (37 boys, 44 girls) wore the Manufacturing Technologies Inc. (MTI) accelerometer for 4 days and kept a previous-day physical activity recall diary in the fall of 2002. Minutes per hour in sedentary, light, and moderate/vigorous activity, as recorded by the MTI, and in nine activity categories, as recorded by the diary, were calculated for three time periods (6:00 am to 2:59 pm, 3:00 pm to 6:59 pm, 7:00 pm to midnight) on each day (Thursday through Sunday). ::: ::: ::: RESULTS ::: Doubly multivariate analysis of variance revealed significant gender by day by time differences in sedentary (p =0.005) and moderate/vigorous (p <0.001) activity, but no significant BMI interactions. Except on Sunday, boys were less sedentary and more active than girls during the late afternoon period. Significant gender by category (p <0.001) and day by category (p <0.001) interactions were also found in the log data. Boys spent more time engaged in TV/electronics and sports, while girls spent more time in personal care. Three activity categories (sports, social interaction, active transportation) stayed at consistent levels across days, while others varied widely by day of the week. ::: ::: ::: CONCLUSIONS ::: Except on Sunday, consistent gender differences were found in activity levels, especially for the late afternoon period. Significant increases in sitting, TV/electronic games, and chores were seen for weekend days. Results support strategies to reduce sitting and electronic recreation, which may increase physical activity. <s> BIB002 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Demographics <s> Smart phones are quite sophisticated and increasingly incorporate diverse and powerful sensors. One such sensor is the tri-axial accelerometer, which measures acceleration in all three spatial dimensions. The accelerometer was initially included for screen rotation and advanced game play, but can support other applications. In prior work we showed how the accelerometer could be used to identify and/or authenticate a smart phone user [11]. In this paper we extend that prior work to identify user traits such as sex, height, and weight, by building predictive models from labeled accelerometer data using supervised learning methods. The identification of such traits is often referred to as "soft biometrics" because these traits are not sufficiently distinctive or invariant to uniquely identify an individual---but they can be used in conjunction with other information for identification purposes. While our work can be used for biometric identification, our primary goal is to learn as much as possible about the smart phone user. This mined knowledge can be then be used for a number of purposes, such as marketing or making an application more intelligent (e.g., a fitness app could consider a user's weight when calculating calories burned). <s> BIB003 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Demographics <s> This paper presents an approach for gender recognition using behavioral biometrics in smartphones. Specifically, this work investigates gender recognition using gait data acquired from the inbuilt accelerometer and gyroscope sensors of a smartphone. The proposed approach involves computation of curvature of the gait signals. In order to capture the local variations of estimated curvatures, we employed histogram features of multi-level local pattern (MLP) and local binary pattern (LBP). In this work, support vector machine (SVM) and aggregate bootstrapping (bagging) classifiers are employed for identification of gender based on the extracted features. Performance evaluation of the proposed approach on a database of 252 gait data collected from 42 subjects yielded promising results. Our experimental results also show that MLP performs better than LBP for feature extraction, while bagging outperforms SVM for classification. <s> BIB004 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Demographics <s> Side-channel attacks revealing the sensitive user data through the motion sensors (such as accelerometer, gyroscope, and orientation sensors) emerged as a new trend in the smartphone security. In this respect, recent studies have examined feasibility of inferring user's tap input by utilizing the motion sensor readings and propounded that some user secrets can be deduced by adopting the different side-channel attacks. More precisely, in this kind of attacks, a malware processes outputs of these sensors to exfiltrate victims private information such as PINs, passwords or unlock patterns. In this paper, we describe a new side-channel attack on smartphones that aims to predict the age interval of the user. Unlike the previous works, our attack does not directly deal with recovering a target user's some secret, rather its sole purpose is determining whether she is a child or an adult. The main idea behind our study relies on the key observation that the characteristics of children and adults differ in hand holding and touching the smartphones. Consequently, we show that there is an apparent correlation between the motion sensor readings and these characteristics that build up our attack strategy. In order to exhibit efficiency of the proposed attack, we have developed an Android application named as BalloonLogger that evaluates accelerometer sensor data and perform child/adult detection with a success rate of 92.5%. To the best of our knowledge, in this work, for the first time, we point out such a security breach. <s> BIB005
Estimates of demographic variables such as age and gender can be made based on data from body-worn accelerometers. It has long been demonstrated that adults and children differ in their smoothness of walking, which is reflected in accelerometer readings  . Menz, Lord and Fitzpatrick compared gait features between young and elder subjects using acceleration signals and discovered that younger subjects showed greater step length, higher velocity and smaller step timing variability  . Using data from accelerometers in smartphones, Davarci et al. were able to predict the age interval of test subjects with a success rate of 92.5% BIB005 . Their work is based on the observation that children and adults differ in the way they hold and touch smartphones. Experimental results by Cho, Park and Kwon indicate that there are also gender-specific movement patterns  . In accordance, research has shown that it is possible to estimate the sex of individuals based on hip movements BIB003 , gait features BIB004 and physical activity patterns BIB002 , all derived from accelerometer data. An experiment also revealed that female gait patterns are significantly influenced by the heel height of their shoes BIB001 . Weiss and Lockhart emphasize that accelerometer-based gender recognition can work independently of a subject's weight and height BIB003 . Even acoustic vibrations caused by a person's voice and captured through a smartphone accelerometer can be used to classify speakers into male and female with high accuracy [35] .