Python general purpose human motion inertial data processing package.
Project description
Scikit Digital Health (SKDH) is a Python package with methods for ingesting and analyzing wearable inertial sensor data.
Documentation: https://scikit-digital-health.readthedocs.io/en/latest/
Bug reports: https://github.com/PfizerRD/scikit-digital-health/issues
Contributing: https://scikit-digital-health.readthedocs.io/en/latest/src/dev/contributing.html
SKDH provides the following:
Methods for ingesting data from binary file formats (ie Axivity, GeneActiv)
Preprocessing of accelerometer data
Common time-series signal features
Common time-series/inertial data analysis functions
Inertial data analysis algorithms (ie gait, sit-to-stand, sleep, activity)
Build Requirements
As of 0.9.15, Scikit Digital Health is built using Meson.
Citation
If you use SKDH in your research, please include the following citation:
Adamowicz, Y. Christakis, M. D. Czech, and T. Adamusiak, “SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing,” JMIR mHealth and uHealth, vol. 10, no. 4, p. e36762, Apr. 2022, doi: 10.2196/36762.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for scikit_digital_health-0.11.0.post1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5015e44a63e942d6fe4e9f845f17d62ee15adc146046cc3953bb6522f917d3a1 |
|
MD5 | 41d8ce14d21940dad003e6574c2139cf |
|
BLAKE2b-256 | d15c06143c50e96c843d6ca69f860ae078aedeb91da17394e2d66af57a3979a8 |
Hashes for scikit_digital_health-0.11.0.post1-cp310-cp310-macosx_10_16_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4ad5781bf74d7aaa2c86e4b7f860359d4c5b2db7dfcccbf2cadbde5b11c5e12b |
|
MD5 | 79fae4669548d52915f55d0ef1086682 |
|
BLAKE2b-256 | 120db5a6fdc817f4cf82c80c901daa51863075a4f319f52cd627192857872a13 |
Hashes for scikit_digital_health-0.11.0.post1-cp39-cp39-macosx_10_16_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8d39af461302280811b7a6c364808a03fe32c36e06be569cc1beadcfdcb45553 |
|
MD5 | 2924547139dba4a9a295d3ae1c201683 |
|
BLAKE2b-256 | 1f49ed2fc1804ceccbb9a80bb017812240ce29671fdd4a389e78f9efcd7e2044 |
Hashes for scikit_digital_health-0.11.0.post1-cp38-cp38-macosx_10_16_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ff5dd545c89e804c618852db1f95b0d8fe298c59a943f4c03fe3ceae89ef81c8 |
|
MD5 | ec48321f29ee9b2ca4b9858e58fa9250 |
|
BLAKE2b-256 | 0eacacadb617ec860ff892a2d110f44a2d73003646f2a728a523c3ab8cbae965 |
Hashes for scikit_digital_health-0.11.0.post1-cp37-cp37m-macosx_10_16_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a9fd66c7e88785b51c8feaabc23b9f54eca86d4f4985c311b3caaee2118aa859 |
|
MD5 | c84d0937f907638cb2180e73909b530c |
|
BLAKE2b-256 | 76a93437fca77f8e3da1a9305a81b5cf713f14159b01e328e756c99afa76f05c |