Accelerometer-determined peak cadence and weight status in children from São Caetano do Sul, Brazil

Ferrari, Gerson Luis de Moraes; Araújo, Timóteo Leandro; Oliveira, Luís C.; Matsudo, Victor Keihan Rodrigues; Mire, Emily F.; Barreira, Tiago V.; Tudor–Locke, Catrine; Katzmarzyk, Peter T.

doi:10.1590/1413-812320172211.21962015

Cited by 2 publications

(2 citation statements)

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“…Using accelerometer data from smartphones, researchers were able to derive an approximation of the body weight and height of users [56,57]. A strong correlation has been observed between accelerometer-determined physical activity and obesity [58].…”

Section: Inference Of Health Parameters and Body Featuresmentioning

confidence: 99%

Privacy implications of accelerometer data

Kröger

Raschke

Bhuiyan

2019

Proceedings of the 3rd International Conference on Cryptography, Security and Privacy

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Accelerometers are sensors for measuring acceleration forces. They can be found embedded in many types of mobile devices, including tablet PCs, smartphones, and smartwatches. Some common uses of built-in accelerometers are automatic image stabilization, device orientation detection, and shake detection. In contrast to sensors like microphones and cameras, accelerometers are widely regarded as not privacy-intrusive. This sentiment is reflected in protection policies of current mobile operating systems, where third-party apps can access accelerometer data without requiring security permission. It has been shown in experiments, however, that seemingly innocuous sensors can be used as a side channel to infer highly sensitive information about people in their vicinity. Drawing from existing literature, we found that accelerometer data alone may be sufficient to obtain information about a device holder's location, activities, health condition, body features, gender, age, personality traits, and emotional state. Acceleration signals can even be used to uniquely identify a person based on biometric movement patterns and to reconstruct sequences of text entered into a device, including passwords. In the light of these possible inferences, we suggest that accelerometers should urgently be re-evaluated in terms of their privacy implications, along with corresponding adjustments to sensor protection mechanisms.

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Section: Inference Of Health Parameters and Body Featuresmentioning

confidence: 99%

Privacy implications of accelerometer data

Kröger

Raschke

Bhuiyan

2019

Proceedings of the 3rd International Conference on Cryptography, Security and Privacy

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“…In contrast to subjective self-reports, accelerometer measurements currently represent the most accurate means for assessing PA and SB [ 10 , 11 ]. With these measurements, we can get specific information about the intensity of PA, e.g., peak cadence [ 12 ], the different profiles on daily PA and SB, [ 13 , 14 ], and the accurate MVPA amounts expressed in metabolic equivalents (MET) [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Accelerometer-Measured Physical Activity Levels and Patterns Vary in an Age- and Sex-Dependent Fashion among Finnish Children and Adolescents

Jussila

Husu

Vähä‐Ypyä

et al. 2022

IJERPH

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Background: The purpose of this study was to measure physical activity (PA), sedentary behavior (SB), and hour-by-hour PA patterns with an accelerometer in a population-based sample of Finnish children and adolescents. Methods: A total of 3274 participants (3rd, 5th, 7th, 9th graders) from 176 schools wore a hip-worn triaxial accelerometer for seven days during waking hours. Mean amplitude deviation of the acceleration data was used to assess PA intensity that was converted to metabolic equivalents and categorized into light, moderate, and vigorous PA. Angle for posture estimation was used to measure SB and standing. Results: The majority of participants’ PA consisted of light PA, and they were sedentary for more than half of their waking hours. Children were more active than adolescents, and boys were more active than girls. Participants took, on average, 9890 steps daily, and one third met the PA recommendation. The participants were divided into tertiles based on daily steps to investigate the variation in PA patterns. Compared to the least active tertile, the most active tertile took twice as many steps on weekdays and nearly three times as many steps on the weekend. Conclusions: The majority of the participants were not active enough, and there was a great variation in PA levels and patterns, especially among the adolescents and on weekends.

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Accelerometer-determined peak cadence and weight status in children from São Caetano do Sul, Brazil

Cited by 2 publications

References 39 publications

Privacy implications of accelerometer data

Privacy implications of accelerometer data

Accelerometer-Measured Physical Activity Levels and Patterns Vary in an Age- and Sex-Dependent Fashion among Finnish Children and Adolescents

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