Package: MIMSunit 0.11.2

Qu Tang

MIMSunit: Algorithm to Compute Monitor Independent Movement Summary Unit (MIMS-Unit)

The MIMS-unit algorithm is developed to compute Monitor Independent Movement Summary Unit, a measurement to summarize raw accelerometer data while ensuring harmonized results across different devices. It also includes scripts to reproduce results in the related publication (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) <doi:10.1123/jmpb.2018-0068>).

Authors:Qu Tang [aut, cre], Dinesh John [aut], Stephen Intille [aut], mHealth Research Group [cph]

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MIMSunit.pdf |MIMSunit.html✨
MIMSunit/json (API)
NEWS

# Install 'MIMSunit' in R:
install.packages('MIMSunit', repos = c('https://mhealthgroup.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mhealthgroup/mimsunit/issues

Datasets:
  • conceptual_diagram_data - The input accelerometer data used to generate the conceptual diagram (Figure 1) in the manuscript.
  • cv_different_algorithms - Coefficient of variation values for different acceleration data summary algorithms
  • edge_case - A short snippet of raw accelerometer signal from a device that has ending data maxed out.
  • measurements_different_devices - The mean and standard deviation of accelerometer summary measure for different acceleration data summary algorithms and for different devices.
  • rest_on_table - A short snippet of raw accelerometer signal from a device resting on a table.
  • sample_raw_accel_data - Sample raw accelerometer data

On CRAN:

3.90 score 10 stars 16 scripts 258 downloads 34 exports 86 dependencies

Last updated 2 years agofrom:254fd53fe0. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-winOKNov 12 2024
R-4.5-linuxOKNov 12 2024
R-4.4-winOKNov 12 2024
R-4.4-macOKNov 12 2024
R-4.3-winOKNov 12 2024
R-4.3-macOKNov 12 2024

Exports:aggregate_for_mimsaggregate_for_orientationbandlimited_interpclip_datacompute_orientationcustom_mims_unitcut_off_signalexport_to_actilifeextrapolateextrapolate_rateextrapolate_single_colgenerate_interactive_plotiirillustrate_extrapolationillustrate_signalimport_actigraph_count_csvimport_actigraph_csvimport_actigraph_csv_chunkedimport_actigraph_metaimport_activpal3_csvimport_enmo_csvimport_mhealth_csvimport_mhealth_csv_chunkedinterpolate_signalmims_unitmims_unit_from_filesparse_epoch_stringsampling_ratesegment_datasensor_orientationsshiny_appsimulate_new_datasum_upvector_magnitude

Dependencies:base64encbitbit64bitopsbslibcachemcaToolsclicliprcolorspacecommonmarkcpp11crayondigestdplyrdygraphsevaluatefansifarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhmshtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelifecyclelubridatemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigplyrprettyunitsprogresspromisesR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppreadrrlangrmarkdownsassscalesshinysignalsourcetoolsstringistringrtibbletidyselecttimechangetinytextzdbutf8vctrsviridisLitevroomwithrxfunxtablextsyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Aggregate over epoch to get numerically integrated values.aggregate_for_mims
Aggregate over epoch to get estimated accelerometer orientation.aggregate_for_orientation
Apply a bandlimited interpolation filter to the signal to change the sampling ratebandlimited_interp
Clip dataframe to the given start and stop timeclip_data
Estimate the accelerometer orientationcompute_orientation
The input accelerometer data used to generate the conceptual diagram (Figure 1) in the manuscript.conceptual_diagram_data
Compute both MIMS-unit and sensor orientations with custom settingscustom_mims_unit
Cut off input multi-channel signal according to a new dynamic rangecut_off_signal
Coefficient of variation values for different acceleration data summary algorithmscv_different_algorithms
A short snippet of raw accelerometer signal from a device that has ending data maxed out.edge_case
Export accelerometer data in Actilife RAW CSV formatexport_to_actilife
Extrapolate input multi-channel accelerometer dataextrapolate extrapolate_single_col
Get extrapolation rate.extrapolate_rate
Plot MIMS unit values or raw signal using dygraphs interactive plotting library.generate_interactive_plot
Apply IIR filter to the signaliir
Plot illustrations about extrapolation in illustration style.illustrate_extrapolation
Plot given raw signal in illustration diagram style.illustrate_signal
Import Actigraph count data stored in Actigraph summary csv formatimport_actigraph_count_csv
Import raw multi-channel accelerometer data stored in Actigraph raw csv formatimport_actigraph_csv
Import large raw multi-channel accelerometer data stored in Actigraph raw csv format in chunksimport_actigraph_csv_chunked
Import The meta information stored in Actigraph RAW or summary csv file.import_actigraph_meta
Import raw multi-channel accelerometer data stored in ActivPal3 csv formatimport_activpal3_csv
Import ENMO data stored in csv csvimport_enmo_csv
Import raw multi-channel accelerometer data stored in mHealth Specificationimport_mhealth_csv
Import large raw multi-channel accelerometer data stored in mHealth Specification in chunks.import_mhealth_csv_chunked
Interpolate missing points and unify sampling rate for multi-channel signalinterpolate_signal
The mean and standard deviation of accelerometer summary measure for different acceleration data summary algorithms and for different devices.measurements_different_devices
Compute Monitor Independent Motion Summary unit (MIMS-unit)mims_unit mims_unit_from_files
Parse epoch string to the corresponding number of samples it represents.parse_epoch_string
A short snippet of raw accelerometer signal from a device resting on a table.rest_on_table
Sample raw accelerometer datasample_raw_accel_data
Estimate sampling rate for multi-channel signalsampling_rate
Segment input dataframe into windows as specified by breaks. 'segment_data' segments the input sensor dataframe into epoch windows with length specified in breaks.segment_data
Estimates sensor orientationsensor_orientations
Run shiny app to compute MIMSunit values from filesshiny_app
Simulate new data based on the given multi-channel accelerometer datasimulate_new_data
Sum of multi-channel signal.sum_up
Vector magnitude of multi-channel signal.vector_magnitude