Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <s> ABSTRACTCROUTER, S. E., P. L. SCHNEIDER, M. KARABULUT, and D. R. BASSETT, JR. Validity of 10 Electronic Pedometers for Measuring Steps, Distance, and Energy Cost. Med. Sci. Sports Exerc., Vol. 35, No. 8, pp. 1455–1460, 2003.PurposeThis study examined the effects of walking speed on the accuracy and <s> BIB001 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <s> The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures. <s> BIB002 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <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> BIB003 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <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> BIB004 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <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> BIB005 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <s> Automatic identification of human activity has led to a possibility of providing personalised services in different domains i.e. healthcare, security and sport etc. With advancement in sensor technology, automatic activity recognition can be done in an unobtrusive and non-intrusive way. The placement of the sensor and wearability are ones of vital keys in the successful activity recognition of free space livings. Experiments were carried out to investigate the use of a single wrist-worn accelerometer for automatic activity classification. The performances of two classification algorithms namely Decision Tree C4.5 and Artificial Neural Network were compared using four different sets of features to classify five daily living activities. The result revealed that Decision Tree C4.5 has outperformed Neural Network regardless of the different sets of features used. The best classification result was achieved using the set containing the most popular and accurate features i.e. mean, minimum, energy and sample differences etc. The best accuracy of 94.13% was achieved using only wrist-worn accelerometer showing a possibility of automatic activity classification with no movement constrain, discomfort and stigmatisation caused by the sensor. <s> BIB006 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <s> Abstract Objectives To systematically review the literature on physical activity variables derived from body-worn sensors during long term monitoring in healthy and in-care older adults. Methods Using pre-designed inclusion and exclusion criteria, a PubMed search strategy was designed to trace relevant reports of studies. Last search date was March 8, 2011. Study selection Studies that included persons with mean or median age of >65 years, used accelerometer-based body-worn sensors with a monitoring length of >24 h, and reported values on physical activity in the samples assessed. Results 1403 abstracts were revealed and 134 full-text papers included in the final review. A variety of variables derived from activity counts or recognition of performed activities were reported in healthy older adults as well as in in-care older adults. Three variables were possible to compare across studies, level of Energy Expenditure in kcal per day and activity recognition in terms of total time in walking and total activity. However, physical activity measured by these variables demonstrated large variation between studies and did not distinguish activity between healthy and in-care samples. Conclusion There is a rich variety in methods used for data collection and analysis as well as in reported variables. Different aspects of physical activity can be described, but the variety makes it challenging to compare across studies. There is an urgent need for developing consensus on activity monitoring protocols and which variables to report. <s> BIB007 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <s> This paper presents a novel investigation of how motion as measured with just a single wearable accelerometer is informative of people's interests and motivation during crowded social events. We collected accelerometer readings on a large number of people (32 and 46 people in two crowded social events involving up to hundreds of people). In our experiments, we demonstrate how people's movements are informative of their particular interests: during talks, their interests in particular topics, and during networking events, their interest to participate successfully to make new contacts and foster existing ones. To our knowledge, using a single body worn accelerometer to measure and automatically infer these aspects of social behaviour has never been attempted before. Our experiments show that despite the challenge of the proposed task, useful automated predictions are possible and demonstrate the potential for further research in this area. <s> BIB008 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <s> Falls, heart attack and stroke are among the leading causes of hospitalization for the elderly and illness individual. The chances of surviving a fall, heart attack or stroke are much greater if the senior gets help within an hour. In this project, a smart elderly home monitoring system (SEHMS) is designed and developed. An Android-based smart phone with 3-axial accelerometer is used as the telehealth device which could detect a fall of the carrier. The smart phone is then connected to the monitoring system by using the TCP/IP networking method via Wi-Fi. A graphical user interface (GUI) is developed as the monitoring system which exhibits the information gathered from the system. In addition, the concept of a remote panic button has been tested and implemented in this project by using the same android based smart phone. With the developed system, elderly and chronically ill patients could stay independently in their own home with care facilities and secure in the knowledge that they are being monitored. <s> BIB009 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <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> BIB010 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <s> In India, an increasing number of vehicles on the roads, in recent past, have led to an increase in the number of road accidents. There have been alarming statistics regarding the number of accidents per day in India. At least 1,42,000 people died due to road accidents in India in the year 2011. Bad driving, lax traffic control, and poor road conditions are the main reason for this. In this work, we present a mobile phone application that uses combination of in-built sensors, GPS, micro-phone and accelerometer, to detect driving behavior along with road and traffic conditions. This application will prove helpful in detecting bad driving as well as road and traffic conditions in order to assist a willing individual to change his or her driving behavior. The law and enforcement agencies may also use this data in analyzing the ground realities for increasing number of road accidents. <s> BIB011 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <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> BIB012 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <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> BIB013 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <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> BIB014 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <s> Heavy loads increase the risk of musculoskeletal injury for foot soldiers and first responders. Continuous monitoring of load carriage in the field has proven difficult. We propose an algorithm for estimating load from a single body-worn accelerometer. The algorithm utilizes three different methods for characterizing torso movement dynamics, and maps the extracted dynamics features to load estimates using two machine learning multivariate regression techniques. The algorithm is applied, using leave-one-subject-out cross-validation, to two field collections of soldiers and civilians walking with varying loads. Rapid, accurate estimates of load are obtained, demonstrating robustness to changes in equipment configuration, walking conditions, and walking speeds. On soldier data with loads ranging from 45 to 89 lbs, load estimates result in mean absolute error (MAE) of 6.64 lbs and correlation of r = 0.81. On combined soldier and civilian data, with loads ranging from 0 to 89 lbs, results are MAE = 9.57 lbs and r = 0.91. <s> BIB015 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <s> BACKGROUND ::: Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus. ::: ::: ::: OBJECTIVES ::: The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified. ::: ::: ::: METHODS ::: Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015. ::: ::: ::: RESULTS ::: The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific. ::: ::: ::: CONCLUSION ::: This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data. ::: ::: ::: PROSPERO REGISTRATION NUMBER ::: CRD42016039991. <s> BIB016 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Activity and Behavior Tracking <s> STUDY OBJECTIVES ::: The validity of consumer-targeted wrist-worn sleep measurement systems has been little studied in children and adolescents. We examined the validity of a new fitness tracker (PFT) manufactured by Polar Electro Oy and the previously validated Actiwatch 2 (AW2) from Philips Respironics against polysomnography (PSG) in children and adolescents. ::: ::: ::: METHODS ::: Seventeen children (age 11.0 ± 0.8 years) and 17 adolescents (age 17.8 ± 1.8 years) wore the PFT and AW2 concurrently with an ambulatory PSG in their own home for 1 night. We compared sleep onset, offset, sleep interval (time from sleep on to offset), actual sleep time (time scored as sleep between sleep on and offset), and wake after sleep onset (WASO) between accelerometers and PSG. Sensitivity, specificity, and accuracy were calculated from the epoch-by-epoch data. ::: ::: ::: RESULTS ::: Both devices performed adequately against PSG, with excellent sensitivity for both age groups (> 0.91). In terms of specificity, the PFT was adequate in both groups (> 0.77), and AW2 adequate in children (0.68) and poor in adolescents (0.58). In the younger group, the PFT underestimated actual sleep time by 29.9 minutes and AW2 underestimated actual sleep time by 43.6 minutes. Both overestimated WASO, PFT by 24.4 minutes and AW2 by 20.9 minutes. In the older group, both devices underestimated actual sleep time (PFT by 20.6 minutes and AW2 by 26.8 minutes) and overestimated WASO (PFT by 12.5 minutes and AW2 by 14.3 minutes). Both devices were accurate in defining sleep onset. ::: ::: ::: CONCLUSIONS ::: This study suggests that this consumer-targeted wrist-worn device performs as well as, or even better than, the previously validated AW2 against PSG in children and adolescents. Both devices underestimated sleep but to a lesser extent than seen in many previous validation studies on research-targeted accelerometers. <s> BIB017
A wide range of physical activity variables and behavior-related information can be derived from raw accelerometer data. Accelerometer-based pedometers ("step counters"), for instance, register the impacts produced by steps during motion and can estimate energy expenditure and distance walked BIB001 . In medical studies, wearable devices with embedded accelerometers are Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. ICCSP 2019, January 19-21, 2019 widely used to assess the amount of sedentary time and physical activity among patients BIB016  . Body-worn accelerometers have also been shown to enable realtime body posture and activity classification. High recognition accuracy has been achieved for basic physical activities, including running, walking, cycling, lying, climbing stairs, falling, sitting and standing BIB006 BIB012 BIB004 BIB009 , as well as for more complex activities, such as writing, reading, typing, painting, sorting paperwork or searching the internet BIB010 . Not only the type but also the duration of activities and temporal behavior patterns can be derived from acceleration signals  BIB007 . When worn during the night, mobile devices with built-in accelerometers may enable sleep-wake cycle monitoring, through variables such as sleep onset and offset, total sleep time and sleep intervals  BIB017 , as well as the monitoring of sleep-related behaviors BIB016 . Accelerometers in handheld and wrist-worn devices can further be used to detect specific hand gestures BIB002 , eating and drinking moments  BIB003 , and smoking BIB014  . Gait features of subjects, extracted from accelerometer data, can even reveal their level of intoxication. Researchers were able to distinguish "sober walk" from "intoxicated walk"  and to estimate blood alcohol content  as well as the number of drinks consumed  via accelerometry alone. In BIB010 , signals from a single body-worn accelerometer were used to detect if a subject is carrying a load. Accelerometer-based gait dynamics have also been used to estimate the weight of carried objects with robustness to variations in walking speeds, body types and walking conditions BIB015 . When located inside a car, motion sensors can be used to measure an operator's driving behavior. In BIB011 , Singh, Juneja and Kapoor identified events such as sudden breaking, sudden acceleration, right and left turns and lane changes from patterns in accelerometer data, as is illustrated in Fig. 1 . From such information, researchers were able to detect aggressive or unsafe driving styles BIB013 and drunk driving patterns BIB005 . Based on indicative body movements and sound vibrations, both measured using accelerometers, researchers were able to derive speech activity and social interactions of subjects  BIB008 . Even ways of reconstructing speech solely from recorded vibrations have been explored. AccelWord, developed in [35] , can detect hotwords spoken by a user, utilizing accelerometer data from commercially available mobile devices. Patents have already been filed for a "method of detecting a user's voice activity using an accelerometer"  and a "system that uses an accelerometer in a mobile device to detect hotwords"  .