Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Health Parameters and Body Features <s> Objective: To describe the cross-sectional relationship between an objective measure of walking (pedometer-determined steps/day) and general indicators of health, a prior diagnosis of one or more components of the metabolic syndrome, and self-reported occupational activity in a generally sedentary working population. ::: ::: ::: ::: Research Methods and Procedures: Steps/day were compared with previous diagnosis of one or more components of the metabolic syndrome (by self-administered questionnaire) and with general health indicators including BMI, waist circumference, resting heart rate, and blood pressure in 182 subjects in Prince Edward Island, Canada. Study participants were volunteer employees recruited from five workplaces where, in general, the job types were moderately or highly sedentary. ::: ::: ::: ::: Results: Steps/day were 7230 ± SD 3447 for women (n = 153) and 8265 ± 2849 (n = 21) for men. Pedometer-determined steps/day were associated inversely with BMI (r = −0.4005, p < 0.0001) in all participants and waist circumference in females only (r = −0.4303, p < 0.0001). There was a low correlation between steps/day and diastolic blood pressure in the whole sample (r = −0.2140, p = 0.0383). Participants who reported a prior diagnosis of one or more components of the metabolic syndrome (hypertension, hypercholesterolemia, heart disease, or type 2 diabetes) took fewer steps/day than healthy participants (p = 0.0254). Pedometer-determined steps/day were positively associated with self-reported occupational activity (p = 0.0002). ::: ::: ::: ::: Discussion: Fewer steps/day are associated with increased BMI, waist circumference, diastolic blood pressure, and components of the metabolic syndrome. Low occupational activity is a contributing factor to low total ambulatory activity. <s> BIB001 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Health Parameters and Body Features <s> A healthy amount of sleep is paramount to leading a healthy and productive lifestyle. Although chronic sleep loss is common in today's society, many people are unaware of the potential adverse health effects of habitual sleep restriction. Under strict experimental conditions, short-term restriction of sleep results in a variety of adverse physiologic effects, including hypertension, activation of the sympathetic nervous system, impairment of glucose control, and increased inflammation. A variety of epidemiologic studies have also suggested an association between self-reported sleep duration and long-term health. Individuals who report both an increased (>8 h/d) or reduced (<7 h/d) sleep duration are at modestly increased risk of all-cause mortality, cardiovascular disease, and developing symptomatic diabetes. Although the data are not definitive, these studies suggest that sleep should not be considered a luxury, but an important component of a healthful lifestyle. <s> BIB002 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Health Parameters and Body Features <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 Health Parameters and Body Features <s> Wrist-worn accelerometers are increasingly being used for the assessment of physical activity in population studies, but little is known about their value for sleep assessment. We developed a novel method of assessing sleep duration using data from 4,094 Whitehall II Study (United Kingdom, 2012-2013) participants aged 60-83 who wore the accelerometer for 9 consecutive days, filled in a sleep log and reported sleep duration via questionnaire. Our sleep detection algorithm defined (nocturnal) sleep as a period of sustained inactivity, itself detected as the absence of change in arm angle greater than 5 degrees for 5 minutes or more, during a period recorded as sleep by the participant in their sleep log. The resulting estimate of sleep duration had a moderate (but similar to previous findings) agreement with questionnaire based measures for time in bed, defined as the difference between sleep onset and waking time (kappa = 0.32, 95%CI:0.29,0.34) and total sleep duration (kappa = 0.39, 0.36,0.42). This estimate was lower for time in bed for women, depressed participants, those reporting more insomnia symptoms, and on weekend days. No such group differences were found for total sleep duration. Our algorithm was validated against data from a polysomnography study on 28 persons which found a longer time window and lower angle threshold to have better sensitivity to wakefulness, while the reverse was true for sensitivity to sleep. The novelty of our method is the use of a generic algorithm that will allow comparison between studies rather than a "count" based, device specific method. <s> BIB004 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Health Parameters and Body Features <s> This paper focuses on the sensing of physiological sound on human body using accelerometers. The physiological sound sensing have demanding requirements on the sensitivity/noise performance of accelerometers since the physiological sounds are usually very weak. In this paper, a piezoelectric accelerometer based on the asymmetrically gapped cantilever structure, which exhibits significantly improved sensitivity, is presented. Furthermore, in order to reduce the package size of the accelerometer, a flip-chip piezoelectric technology is proposed. The accelerometer has a resonant frequency of 1580 Hz which is much higher than the heart sound frequency range, and a quality factor of 9.2. Using a coherent scaling method, the scaled noise level of the accelerometer between 10 Hz and 400 Hz is 1 µV/√Hz. Preliminarily test results show that the signal-to-noise ratio of the heart sound signal measured by the designed accelerometer is about four times higher than that by a high-end stethoscope. <s> BIB005 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Health Parameters and Body Features <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> BIB006 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Health Parameters and Body Features <s> Discrimination between normal and pathological voice is a critical component in laryngeal pathology diagnosis and vocal rehabilitative treatment. In the present study, a portable miniature glottal notch accelerometer (GNA) device with supervised machine learning techniques was proposed to discriminate between three human voice types: normal, breathy, and pressed voice. Fourteen native American English speakers who were wearing a GNA device produced five different English single vowels in each of the three voice types. Acoustic features of the GNA signals were extracted using spectral analysis. Preliminary assessments of feature discrepancy among different voice types were made to present physical clues of discrimination. The linear discriminant analysis technique was applied to reduce the dimensionality of the raw-feature vector of the GNA signals. Maximization of between-class distance and minimization of within-class distance were synchronously achieved. The voice types were then classified using severa... <s> BIB007 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Health Parameters and Body Features <s> Background ::: There is growing interest in the area of "wearable tech" and its relationship to health. A common element of many of these devices is a triaxial accelerometer that can yield continuous information on gross motor activity levels; how such data might predict changes in health is less clear. ::: ::: ::: Methods ::: We examined accelerometry data from 2,976 older men who were part of the Osteoporotic Fractures in Men (MrOS) study. Using a shape-naive technique, functional principal component analysis, we examined the patterns of motor activity over the course of 4-7 days and determined whether these patterns were associated with changes in polysomnographic-determined sleep and cognitive function (Trail Making Test-Part B [Trails B], Modified Mini-Mental State Examination [3MS]), as well as mortality over 6.5-8 years of follow-up. ::: ::: ::: Results ::: In comparing baseline to 6.5 years later, multivariate modeling indicated that low daytime activity at baseline was associated with worsening of sleep efficiency (p < .05), more wake after sleep onset (p < .05), and a decrease in cognition (Trails B; p < .001), as well as a 1.6-fold higher rate of all-cause mortality (hazard ratio = 1.64 [1.34-2.00]). Earlier wake and bed times were associated with a decrease in cognition (3MS; p < .05). Having a late afternoon peak in activity was associated with a 1.4-fold higher rate of all-cause mortality (hazard ratio = 1.46 [1.21-1.77]). Those having a longer duration of their daytime activity with a bimodal activity pattern also had over a 1.4-fold higher rate of cardiovascular-related mortality (hazard ratio = 1.42 [1.02-1.98]). ::: ::: ::: Conclusions ::: Patterns of daily activity may be useful as predictive biomarkers for changes in clinically relevant outcomes, including mortality and changes in sleep and cognition in older men. <s> BIB008 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Health Parameters and Body Features <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> BIB009 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Inference of Health Parameters and Body Features <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> BIB010
Body-worn accelerometers can be used to gain insight into a person's physical characteristics and health status. Using accelerometer data from smartphones, researchers were able to derive an approximation of the body weight and height of users BIB003 BIB006 . A strong correlation has been observed between accelerometer-determined physical activity and obesity  . Physical activity is generally recognized as a promoter and indicator of health  . A person's amount of physical activity can reveal sensitive information about latent chronic diseases and the person's degree of mobility  as well as about cognitive function and even risk of mortality BIB008 . As explained in section 2.1, a wide range of activity-related variables can be derived from accelerometer data, including energy expenditure, type of activity and temporal activity patterns. This association is increasingly put to use in health studies, where accelerometers are used to remotely assess the physical activity level of participants BIB001 . Another important factor in population health is the amount of sleep that people get. Sleep loss has been associated with developing serious illnesses, such as cardiovascular disease and diabetes, and even with increased all-cause mortality BIB002 . Numerous studies have shown that accelerometers in wearable devices can be used for evaluating sleep patterns  , sleep fragmentation BIB004 and sleep efficiency BIB009 . Actigraphy, an accelerometer-based assessment method, has been described as an "essential tool in sleep research and sleep medicine"  . Experimental results from Pesonen and Kuula suggest that accelerometers in consumer-targeted wearables can be as effective for sleep monitoring as research-targeted devices BIB010 . Specialized accelerometers have been used to measure various other health parameters, including voice health BIB007 , postural stability  and physiological sound BIB005 .