Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> The suitable placement of a small number of calibrated piezoresistive accelerometer devices may suffice to assess postures and motions reliably. This finding, which was obtained in a previous investigation, led to the further development of this methodology and to an extension from the laboratory to conditions of daily life. The intention was to validate the accelerometric assessment against behavior observation and to examine the retest reliability. Twenty-four participants were recorded, according to a standard protocol consisting of nine postures/motions (repeated once) which served as reference patterns. The recordings were continued outside the laboratory. A participant observer classified the postures and motions. Four sensor placements (sternum, wrist, thigh, and lower leg) were used. The findings indicated that the detection of posture and motion based on accelerometry is highly reliable. The correlation between behavior observation and kinematic analysis was satisfactory, although some participants showed discrepancies regarding specific motions. <s> BIB001 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody’s (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb’s three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy. <s> BIB002 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> Drunk driving, or officially Driving Under the Influence (DUI) of alcohol, is a major cause of traffic accidents throughout the world. In this paper, we propose a highly efficient system aimed at early detection and alert of dangerous vehicle maneuvers typically related to drunk driving. The entire solution requires only a mobile phone placed in vehicle and with accelerometer and orientation sensor. A program installed on the mobile phone computes accelerations based on sensor readings, and compares them with typical drunk driving patterns extracted from real driving tests. Once any evidence of drunk driving is present, the mobile phone will automatically alert the driver or call the police for help well before accident actually happens. We implement the detection system on Android G1 phone and have it tested with different kinds of driving behaviors. The results show that the system achieves high accuracy and energy efficiency. <s> BIB003 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> Mobile devices are becoming increasingly sophisticated and now incorporate many diverse and powerful sensors. The latest generation of smart phones is especially laden with sensors, including GPS sensors, vision sensors (cameras), audio sensors (microphones), light sensors, temperature sensors, direction sensors (compasses), and acceleration sensors. In this paper we describe and evaluate a system that uses phone-based acceleration sensors, called accelerometers, to identify and authenticate cell phone users. This form of behavioral biometrie identification is possible because a person's movements form a unique signature and this is reflected in the accelerometer data that they generate. To implement our system we collected accelerometer data from thirty-six users as they performed normal daily activities such as walking, jogging, and climbing stairs, aggregated this time series data into examples, and then applied standard classification algorithms to the resulting data to generate predictive models. These models either predict the identity of the individual from the set of thirty-six users, a task we call user identification, or predict whether (or not) the user is a specific user, a task we call user authentication. This work is notable because it enables identification and authentication to occur unobtrusively, without the users taking any extra actions-all they need to do is carry their cell phones. There are many uses for this work. For example, in environments where sharing may take place, our work can be used to automatically customize a mobile device to a user. It can also be used to provide device security by enabling usage for only specific users and can provide an extra level of identity verification. <s> BIB004 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest. <s> BIB005 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> Monitoring of physical activities is a growing field with potential applications such as lifecare and healthcare. Accelerometry shows promise in providing an inexpensive but effective means of long-term activity monitoring of elderly patients. However, even for the same physical activity the output of any body-worn Triaxial Accelerometer (TA) varies at different positions of a subject's body, resulting in a high within-class variance. Thus almost all existing TA-based human activity recognition systems require firm attachment of TA to a specific body part, making them impractical for long-term activity monitoring during unsupervised free living. Therefore, we present a novel hierarchical recognition model that can recognize human activities independent of TA's position along a human body. The proposed model minimizes the high within-class variance significantly and allows subjects to carry TA freely in any pocket without attaching it firmly to a body-part. We validated our model using six daily physical activities: resting (sit/stand), walking, walk-upstairs, walk-downstairs, running, and cycling. Activity data is collected from four most probable body positions of TA: chest pocket, front trousers pocket, rear trousers pocket, and inner jacket pocket. The average accuracy of about 95% illustrates the effectiveness of the proposed method. <s> BIB006 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> Accelerometer-based biometric gait recognition offers a convenient way to authenticate users on their mobile devices. Modern smartphones contain in-built accelerometers which can be used as sensors to acquire the necessary data while the subjects are walking. Hence, no additional costs for special sensors are imposed to the user. In this publication we extract several features from the gait data and use the k-Nearest Neighbour algorithm for classification. We show that this algorithm yields a better biometric performance than the machine learning algorithms we previously used for classification, namely Hidden Markov Models and Support Vector Machines. We implemented the presented method on a smartphone and demonstrate that it is efficient enough to be applied in practice. <s> BIB007 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> We show that accelerometer readings are a powerful side channel that can be used to extract entire sequences of entered text on a smart-phone touchscreen keyboard. This possibility is a concern for two main reasons. First, unauthorized access to one's keystrokes is a serious invasion of privacy as consumers increasingly use smartphones for sensitive transactions. Second, unlike many other sensors found on smartphones, the accelerometer does not require special privileges to access on current smartphone OSes. We show that accelerometer measurements can be used to extract 6-character passwords in as few as 4.5 trials (median). <s> BIB008 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> AB Purpose: Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities. Methods: Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2-, 4-, and 12.8-s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject's activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated. Results: With 12.8-s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%. Conclusions: A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency. <s> BIB009 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> Activity recognition is required in various applications such as medical monitoring and rehabilitation. Previously developed activity recognition systems utilizing triaxial accelerometers have provided mixed results, with subject-to-subject variability. This paper presents an accurate activity recognition system utilizing a body worn wireless accelerometer, to be used in the real-life application of patient monitoring. The algorithm utilizes data from a single, waist-mounted triaxial accelerometer to classify gait events into six daily living activities and transitional events. The accelerometer can be worn at any location around the circumference of the waist, thereby reducing user training. Feature selection is performed using Relief-F and sequential forward floating search (SFFS) from a range of previously published features, as well as new features introduced in this paper. Relevant and robust features that are insensitive to the positioning of accelerometer around the waist are selected. SFFS selected almost half the number of features in comparison to Relief-F and provided higher accuracy than Relief-F. Activity classification is performed using Naive Bayes and $k$ -nearest neighbor $(k$ -NN) and the results are compared. Activity recognition results on seven subjects with leave-one-person-out error estimates show an overall accuracy of about 98% for both the classifiers. Accuracy for each of the individual activity is also more than 95%. <s> BIB010 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> Traffic safety and energy efficiency of vehicles are strictly related to driver’s behavior. The scientific literature has investigated on some specific dynamic parameters that, among the others, can be used as a measure of unsafe or aggressive driving style such as longitudinal and lateral acceleration of vehicle. Moreover, the use of modern mobile devices (smartphones and tablets), and their internal sensors (GPS receivers, three-axes accelerometers), allows road users to receive real time information and feedback that can be useful to increase awareness of drivers and promote safety. This paper focuses on the development of a prototype mobile application that can evaluate the grade of safety that drivers are keeping on the road by measuring of accelerations (longitudinal and lateral) and warning for users when it can be convenient to correct their driving style. The aggressiveness is evaluated by plotting vehicle’s acceleration on a g-g diagram specially studied and designed, where horizontal and lateral acceleration is displayed inside areas of “Good Driving Style”. Several experimental tests were carried out with different drivers and cars in order to estimate the system accuracy and the usability of the application. This work is part of the wider research project M2M, Mobile to Mobility: Information and communication technology systems for road traffic safety (PON National Operational Program for Research and Competitiveness 2007-2013) which is based on the use of mobile sensor computing systems for giving real-time information in order to reduce risks and to make the transportation system more safe and comfortable. <s> BIB011 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> While body movement patterns recorded by a smartphone accelerometer are now well understood to be discriminative enough to separate users, little work has been done to address the question of if or how the position in which the phone is held affects user authentication. In this work, we show through a combination of supervised learning methods and statistical tests, that there are certain users for whom exploitation of information of how a phone is held drastically improves classification performance. We propose a two-stage authentication framework that identifies the location of the phone before performing authentication, and show its benefits based on a dataset of 30 users. Our work represents a first step towards bridging the gap between accelerometer-based authentication systems analyzed from the context of a laboratory environment and a real accelerometer-based authentication system in the wild where phone positioning cannot be assumed. <s> BIB012 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors --- breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6. <s> BIB013 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> In this paper, we present the design, implementation and evaluation of a user authentication system, Headbanger, for smart head-worn devices, through monitoring the user's unique head-movement patterns in response to an external audio stimulus. Compared to today's solutions, which primarily rely on indirect authentication mechanisms via the user's smartphone, thus cumbersome and susceptible to adversary intrusions, the proposed head-movement based authentication provides an accurate, robust, light-weight and convenient solution. Through extensive experimental evaluation with 95 participants, we show that our mechanism can accurately authenticate users with an average true acceptance rate of 95.57% while keeping the average false acceptance rate of 4.43%. We also show that even simple head-movement patterns are robust against imitation attacks. Finally, we demonstrate our authentication algorithm is rather light-weight: the overall processing latency on Google Glass is around 1.9 seconds. <s> BIB014 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> The aim of this study is to estimate height of a carrier (owner) of a smartphone by using single three-axis accelerometer signals. We found that the accelerometer signals collected while a carrier goes up the stairs contain features that are correlated with height of the carrier as high as \(r = 0.801\). Apart from potentially useful applications of the result, there might arise a privacy issue in using smartphone since approximate height could be acquired in this way. Although, in general, height is not regarded as private information to be worried about, if someone thinks his/her height belongs to privacy, it should be protected against leakage. <s> BIB015 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> Motion sensors, especially accelerometers, on smartphones have been discovered to be a powerful side channel for spying on users’ privacy. In this paper, we reveal a new accelerometer-based side-channel attack which is particularly serious: malware on smartphones can easily exploit the accelerometers to trace metro riders stealthily. We first address the challenge to automatically filter out metro-related data from a mass of miscellaneous accelerometer readings, and then propose a basic attack which leverages an ensemble interval classifier built from supervised learning to infer the riding trajectory of the user. As the supervised learning requires the attacker to collect labeled training data for each station interval, this attack confronts the scalability problem in big cities with a huge metro network. We thus further present an improved attack using semi-supervised learning, which only requires the attacker to collect labeled data for a very small number of distinctive station intervals. We conduct real experiments on a large self-built dataset, which contains more than 120 h of data collected from six metro lines of three major cities. The results show that the inferring accuracy could reach 89% and 94% if the user takes the metro for four and six stations, respectively. We finally discuss possible countermeasures against the proposed attack. <s> BIB016 </s> Privacy implications of accelerometer data: a review of possible inferences <s> DISCUSSION AND IMPLICATIONS <s> Abstract Objective In field settings, wrist- and waist-worn accelerometers are typically used to assess sleep characteristics and movement behaviors, respectively. There has been a shift in movement behavior studies to wear accelerometers 24 h/d. Sleep characteristics could be assessed in these studies if sleep algorithms were available for waist-worn accelerometers. The objective of this study was to develop and provide validity data for an algorithm/sleep likelihood score cut-off to estimate sleep efficiency in children using the waist-worn Actical accelerometer. Design Cross-sectional study. Participants Fifty healthy children aged 10-13 years. Measurements Children wore an Actical on their waist and an Actiwatch 2 on their nondominant wrist for 8 nights at home in their normal sleep environment. Participants were randomized into algorithm/sleep likelihood score "development" and "test" groups (n=25 per group). Within the development group, we assessed sleep efficiency with the Actical using the same algorithm that the Actiwatch 2 uses and selected the sleep likelihood score cut-off value that was the most accurate at predicting sleep efficiency at the nightly level compared with the Actiwatch 2. We applied this algorithm and cut-off value to the test group. Results Mean (SD) sleep efficiency estimates for the test group from the Actical and Actiwatch 2 were 89.0% (3.9%) and 88.7% (3.1%), respectively. Bland-Altman plots and absolute difference scores revealed considerable agreement between devices for both nightly and weekly estimates of sleep efficiency. Conclusion A waist-worn Actical accelerometer can accurately predict sleep efficiency in field settings among healthy 10- to 13-year-olds. <s> BIB017
As shown in the previous section, accelerometers in mobile devices can allow serious invasions of user privacy. Even when other sensors, such as cameras, microphones and GPS are turned off, accelerometer data can be sufficient to obtain information about a device holder's location, health condition, body features, age, gender, emotions and personality traits. Acceleration signals may even be used to uniquely identify a person based on biometric movement patterns and to reconstruct sequences of text entered into a device. It has to be acknowledged that most experimental studies cited in this paper have substantial limitations. First, many approaches were only tested in controlled laboratory settings BIB010 BIB009 BIB002  BIB011 BIB003 35, BIB007 BIB012 BIB014 BIB008 BIB015 . For methods applied under real-life conditions, considerable reductions in accuracy have been observed  BIB001 . Second, several of the presented methods require prior knowledge about the user or the user's context in order to function BIB016 BIB007 BIB012  BIB014 BIB004  . Third, subjects in some of the experiments wore accelerometers attached to certain body parts, such as chest  BIB005 , hip BIB007 , waist BIB010 , or even multiple body parts BIB002 BIB013 BIB017 , whereas in reality, mobile devices are mostly worn around the wrist  or interchangeably in hands, bags, and pockets BIB006 . In light of these limitations, the real-world applicability of the presented methods can be questioned. On the other hand, it may reasonably be assumed that at least some of the parties who regularly access accelerometer data from consumer devices (e.g. device manufacturers, service providers, app developers) possess larger sets of training data, more technical expertise and more financial resources than the researchers cited in this paper. Furthermore, data from other sensors and auxiliary data may be available to potential adversaries, improving their capability to draw sensitive inferences, while the methods considered in this paper solely rely on accelerometer data. Thus, our work represents only an initial and non-exhaustive exploration of the topic. It would be enough if even one of the identified threats is realized, however, for user privacy to be seriously impacted. Also, it seems probable that the risk will continue to grow with further improvements of sensor technologies in terms of cost, size and accuracy, further advances in machine learning methods, and further proliferation of accelerometer-equipped mobile devices. Given the widespread perception of accelerometers as nonintrusive, we call for an urgent reconsideration of their privacy implications, along with corresponding adjustments to technical and legal protection measures. In our opinion, the sensitivity of sensor data should generally be assessed in consideration of all inferences that could plausibly be drawn from it, and not based on the sensor's official purpose. Further research into the privacyintrusion potential of accelerometers and other seemingly benign sensors is needed, taking into account state-of-the-art data mining techniques. As it is extremely difficult, however, to meaningfully determine the limits of continuously advancing inference methods, most sensors in mobile devices should be regarded and treated as highly sensitive by default.