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)
Availability
SKDH is available on both conda-forge
and PyPI
.
conda install scikit-digital-health -c conda-forge
or
pip install scikit-digital-health
[!WARNING] Windows pre-built wheels are provided as-is, with limited/no testing on changes made to compile extensions for Windows.
[!NOTE] Windows users may need to install an additional requirement: Microsoft Visual C++ redistributable >14.0. The 2015 version can be found here: https://www.microsoft.com/en-us/download/details.aspx?id=53587
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:
[1] L. 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-0.16.0.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 43ad03954f949e9db36c4c2a685563a5b8c8c602491112c465e2c8d43d4555b7 |
|
MD5 | 1ed830fc8d71cc69ff51dd6fb6e1d4c3 |
|
BLAKE2b-256 | 14602983b55825025ea1437774778c7a8e6be4d6dc648ee8923a0557b4b12bda |
Hashes for scikit_digital_health-0.16.0-cp312-cp312-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8342b691dee43f13112941ac9e808a9a6b1d33bce540989b811eba7ee8725f88 |
|
MD5 | 5b82a2989efb22f4caa383ddd1d414b2 |
|
BLAKE2b-256 | 23909b7e5b8955fc61f915efb6e968ef6a531ae537e2a4cd500d6db350b8b56b |
Hashes for scikit_digital_health-0.16.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | acdd150d895851e86c59a1ce64e7a83fc943499232fdf30d318d4961d7df37d4 |
|
MD5 | 0a23d8ab689c805e02164d4d80f5b5d7 |
|
BLAKE2b-256 | bd54399818617145a115e5f037550ef7594ecd6a9b735ce7e3678f39adbdaab9 |
Hashes for scikit_digital_health-0.16.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e3792b2ab2b1d13c2eff06f9fca16835993a04713cecb649e95f09a093e148ea |
|
MD5 | 222ee26f9a60e82c6aec52fb232ec77e |
|
BLAKE2b-256 | ae1d3419aa8def1df4caecd079d79198d704b66a24e9ef71c530f600fd823e17 |
Hashes for scikit_digital_health-0.16.0-cp311-cp311-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a1a2ce25f078dfa995fffc28318cf77fc37fa89c837834be61ea55f7251c54e1 |
|
MD5 | c275f82f607849559483bb8d7b7e177b |
|
BLAKE2b-256 | 7ec1458c419b85cf98d8d9adae37a0856f330e78d8d2f96cec68933a3e9a002c |
Hashes for scikit_digital_health-0.16.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a253c38731261d0b8dcacd88ccc3635e39654775b66a28e8a97668bdc63fb410 |
|
MD5 | 9a3636ff455fe5324d129e50b0a09a4e |
|
BLAKE2b-256 | 0ee24763d3cb5e8f001e58a7f339923af658aa029fa9c0e4858761c45aeb2627 |
Hashes for scikit_digital_health-0.16.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4734ff12c643ba6a578e1def3c02eb44a637efe4b08a28699e65bc5d4076f626 |
|
MD5 | 14f438147cea43d8e1d8cf9192d7e7b5 |
|
BLAKE2b-256 | 45409a26808864ffc792cb4ef6c8f7bb4635619c8432a17887c3859d7c92b869 |
Hashes for scikit_digital_health-0.16.0-cp310-cp310-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 28ec433b184020590601f3287e210f2754bf2777e74fbf2ff487270f63daddee |
|
MD5 | b238c6cbe19b5e057e14df0e4bc40757 |
|
BLAKE2b-256 | e0da0050235d7bdce54b9c685dfdc738c77871593a2cf0a4f7cb0a30da4c9479 |
Hashes for scikit_digital_health-0.16.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3834bc2b62a2d4dbfa34cec4e9cc433be9f0eef41e8f3c28d5c51b42fc1cf176 |
|
MD5 | 1c522b2c8e00a85913d91eb3697d3c51 |
|
BLAKE2b-256 | 006fb8e1477171ba62fdf07c49031f02c497b1f8db6a3139d802d69bfb511ca7 |
Hashes for scikit_digital_health-0.16.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b10b15989240392cf22c36815626b0c0566d9c82e210d1564551de7b5483e732 |
|
MD5 | ff85d4be052df9f05fa38ad4535486f5 |
|
BLAKE2b-256 | fc3e00888e8bf7ed622ec373e2bab1146fd9bd6bde4a976e98e3efe4e2282231 |
Hashes for scikit_digital_health-0.16.0-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ac0b6920c571bbef68cf9152521128e6ce52a84a4c7150938e60b49095c2e320 |
|
MD5 | d762e831d8ca41afb22fbe93e52b53df |
|
BLAKE2b-256 | 61593121fa936d736c97baa917baf7c5e47b7254d705b990251a838d6f4d3e58 |
Hashes for scikit_digital_health-0.16.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d081eda24900f67eb59b24d98c059fc3260f4d83cf952df7b7a5bb24133cdfa5 |
|
MD5 | 815bd652b7a1ab83c582c08e90c4cb36 |
|
BLAKE2b-256 | 0f8e4ff61b05249794ed7760ffcf36b81fbec8b7d4ff08c1fc9edee175f76d86 |
Hashes for scikit_digital_health-0.16.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b2f19edc46b023cce0f5fd03661300795c8b350959ce9da28a7210a6d1cde249 |
|
MD5 | 121daf9e95b52bd601409ebfcddde51a |
|
BLAKE2b-256 | fe81af23fc8fe691eb87126280b9e33c259de333d63a6cf3b5f3d581c4a04a48 |