Descriptions of Functional Data

Ramsay, J. O.; Hooker, Giles; Graves, Spencer

doi:10.1007/978-0-387-98185-7_6

Cited by 13 publications

(20 citation statements)

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“…The aim of this approach is to observe the behavior of the MBD measures over time, given a set of scalar covariates (BMI at the ages 20 or 40, menopausal status, age at menarche and birth index). The analysis was conducted using the R “fda” package [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

Lifetime changes in body fatness and breast density in postmenopausal women: the FEDRA study

et al. 2023

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Background High mammographic breast density (MBD) is an established risk factor for breast cancer (BC). Body fatness conveys an increased BC risk in postmenopause but is associated with less dense breasts. Here, we studied the relationship between body fatness and breast composition within the FEDRA (Florence-EPIC Digital mammographic density and breast cancer Risk Assessment) longitudinal study. Methods Repeated anthropometric data and MBD parameters (obtained through an automated software on BC screening digital mammograms) were available for all participants, as well as information on other BC risk factors. Multivariate linear regression and functional data analysis were used to longitudinally evaluate the association of body fatness, and changes thereof over time, with dense (DV) and non-dense (NDV) breast volumes and volumetric percent density (VPD). Results A total of 5,262 women were included, with anthropometric data available at 20 and 40 years of age, at EPIC baseline (mean 49.0 years), and an average of 9.4 years thereafter. The mean number of mammograms per woman was 3.3 (SD 1.6). Body fatness (and increases thereof) at any age was positively associated with DV and NDV (the association being consistently stronger for the latter), and inversely associated with VPD. For instance, an increase by 1 kg/year between the age of 40 years and EPIC baseline was significantly associated with 1.97% higher DV, 8.85% higher NDV, and 5.82% lower VPD. Conclusion Body fatness and its increase from young adulthood until midlife are inversely associated with volumetric percent density, but positively associated with dense and non-dense breast volumes in postmenopausal women.

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Section: Methodsmentioning

confidence: 99%

Lifetime changes in body fatness and breast density in postmenopausal women: the FEDRA study

et al. 2023

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“…Functional Principal Component Analysis (fPCA) is a statistical method to identify functional primitives from time-varying data. In this section, we will provide a brief introduction to the theory, while werefer to [ 35 ] for more details. For the sake of simplicity, since each DoF can be analyzed separately from the others with this method, the equations will be defined for a single joint.…”

Section: Methodsmentioning

confidence: 99%

“…The output of fPCA is a basis of functions

which maximizes the explained variances of joint motions throughout the whole dataset. For more detail on how these fPCs can be extracted, we refer the interested reader to [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

A Multi-Modal Under-Sensorized Wearable System for Optimal Kinematic and Muscular Tracking of Human Upper Limb Motion

Paolo

Baracca

Menolotto

et al. 2023

Sensors

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Wearable sensing solutions have emerged as a promising paradigm for monitoring human musculoskeletal state in an unobtrusive way. To increase the deployability of these systems, considerations related to cost reduction and enhanced form factor and wearability tend to discourage the number of sensors in use. In our previous work, we provided a theoretical solution to the problem of jointly reconstructing the entire muscular-kinematic state of the upper limb, when only a limited amount of optimally retrieved sensory data are available. However, the effective implementation of these methods in a physical, under-sensorized wearable has never been attempted before. In this work, we propose to bridge this gap by presenting an under-sensorized system based on inertial measurement units (IMUs) and surface electromyography (sEMG) electrodes for the reconstruction of the upper limb musculoskeletal state, focusing on the minimization of the sensors’ number. We found that, relying on two IMUs only and eight sEMG sensors, we can conjointly reconstruct all 17 degrees of freedom (five joints, twelve muscles) of the upper limb musculoskeletal state, yielding a median normalized RMS error of 8.5% on the non-measured joints and 2.5% on the non-measured muscles.

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“…In other words, each of the response curve sets were deemed appropriately smooth for this particular application. This process is introduced in Chapter 5 of [ 21 ]. Fig.…”

Section: Methodsmentioning

confidence: 99%

“…An appropriate level of smoothing was determined by visual inspection of the relationship between GCV and DoF in the smoothed model. This procedure is explained in depth in [ 21 ]. This figure shows a minimal GCV when the model contains 350 DoF, which corresponds to a λ near 200.…”

Section: Figmentioning

confidence: 99%

Functional Data Analysis for Predicting Pediatric Failure to Complete Ten Brief Exercise Bouts

Coronato

Brown

Sharma

et al. 2022

IEEE J. Biomed. Health Inform.

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Physiological response to physical exercise through analysis of cardiopulmonary measurements has been shown to be predictive of a variety of diseases. Nonetheless, the clinical use of exercise testing remains limited because interpretation of test results requires experience and specialized training. Additionally, until this work no methods have identified which dynamic gas exchange or heart rate responses influence an individual's decision to start or stop physical activity. This research examines the use of advanced machine learning methods to predict completion of a test consisting of multiple exercise bouts by a group of healthy children and adolescents . All participants could complete the ten bouts at low or moderate-intensity work rates, however, when the bout work rates were highintensity, 50% refused to begin the subsequent exercise bout before all ten bouts had been completed (task failure). We explored machine learning strategies to model the relationship between the physiological time series, the participant's anthropometric variables, and the binary outcome variable indicating whether the participant completed the test. The best performing model, a generalized spectral additive model with functional and scalar covariates, achieved 93.6% classification accuracy and an F1 score of 93.5%. Additionally, functional analysis of variance testing showed that participants in the 'failed' and 'success' groups have significantly different functional means in three signals: heart rate, oxygen uptake rate, and carbon dioxide uptake rate. Overall, these results show the capability of functional data analysis with generalized spectral additive models to identify key differences in the exercise-induced responses of participants in multiple bout exercise testing.

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Descriptions of Functional Data

Cited by 13 publications

References 0 publications

Lifetime changes in body fatness and breast density in postmenopausal women: the FEDRA study

Lifetime changes in body fatness and breast density in postmenopausal women: the FEDRA study

A Multi-Modal Under-Sensorized Wearable System for Optimal Kinematic and Muscular Tracking of Human Upper Limb Motion

Functional Data Analysis for Predicting Pediatric Failure to Complete Ten Brief Exercise Bouts

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