PhysioWearable analyzes free-living tri-axial accelerometry. It covers CSV ingestion, gravity-based calibration, ENMO, non-wear detection, step detection, physical-activity intensity classification, bout summaries, and optional conversion to the common PhysioExperiment model.
Installation
# the containers build on Bioconductor, so its repositories are needed too
install.packages("BiocManager", repos = "https://cloud.r-project.org")
options(repos = c(
xbiosignal = "https://x-biosignal.r-universe.dev",
BiocManager::repositories()
))
install.packages("PhysioWearable")Quick start
library(PhysioWearable)
sampling_rate <- 50
time <- seq(0, 20, by = 1 / sampling_rate)
acceleration <- cbind(
x = 0,
y = 0,
z = 1 + 0.4 * sin(2 * pi * 1.8 * time)
)
enmo <- computeENMO(
acceleration,
sampling_rate = sampling_rate,
epoch_sec = 5,
unit = "mg"
)
steps <- detectSteps(acceleration, sampling_rate)
enmo
steps$n_stepsMain functions
| Stage | Functions |
|---|---|
| Import and model conversion |
readAccelCSV(), accelToPhysioExperiment()
|
| Calibration | autoCalibrateAccel() |
| Movement metric | computeENMO() |
| Wear-time handling | detectNonWear() |
| Ambulation | detectSteps() |
| Intensity and bouts |
paIntensityThresholds(), classifyBouts()
|
Device placement, units, calibration, sampling rate, non-wear parameters, and population-specific intensity thresholds should be recorded with study methods. Default outputs are research summaries, not clinical classifications.
Ecosystem role
PhysioWearable converts raw free-living acceleration into quality-control and activity summaries. Device-specific multi-signal imports belong in PhysioDevices; downstream statistical and clinical analyses can consume the shared model or returned tables.
Citation
citation("PhysioWearable")See the ecosystem governance, support policy, and contribution guide.
Author and license
Author and maintainer: Yusuke Matsui. Licensed under the MIT License.