scikit-digital-health

skdh_badge

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/pfizer-opensource/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

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.

Examples

An example notebook can be found in the examples folder: SKDH tutorial along with some sample data. The tutorial walks through running individual modules in SKDH, then building up a pipeline, and finally creating a new module.

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.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scikit_digital_health-0.17.16.tar.gz (8.4 MB view details)

Uploaded Aug 25, 2026 Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

scikit_digital_health-0.17.16-cp314-cp314t-win_amd64.whl (3.0 MB view details)

Uploaded Aug 25, 2026 CPython 3.14tWindows x86-64

scikit_digital_health-0.17.16-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (2.4 MB view details)

Uploaded Aug 25, 2026 CPython 3.14tmanylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

scikit_digital_health-0.17.16-cp314-cp314t-macosx_26_0_arm64.whl (2.2 MB view details)

Uploaded Aug 25, 2026 CPython 3.14tmacOS 26.0+ ARM64

scikit_digital_health-0.17.16-cp314-cp314-win_amd64.whl (3.0 MB view details)

Uploaded Aug 25, 2026 CPython 3.14Windows x86-64

scikit_digital_health-0.17.16-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (2.4 MB view details)

Uploaded Aug 25, 2026 CPython 3.14manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

scikit_digital_health-0.17.16-cp314-cp314-macosx_26_0_arm64.whl (2.2 MB view details)

Uploaded Aug 25, 2026 CPython 3.14macOS 26.0+ ARM64

scikit_digital_health-0.17.16-cp313-cp313-win_amd64.whl (3.0 MB view details)

Uploaded Aug 25, 2026 CPython 3.13Windows x86-64

scikit_digital_health-0.17.16-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (2.4 MB view details)

Uploaded Aug 25, 2026 CPython 3.13manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

scikit_digital_health-0.17.16-cp313-cp313-macosx_26_0_arm64.whl (2.2 MB view details)

Uploaded Aug 25, 2026 CPython 3.13macOS 26.0+ ARM64

scikit_digital_health-0.17.16-cp312-cp312-win_amd64.whl (3.0 MB view details)

Uploaded Aug 25, 2026 CPython 3.12Windows x86-64

scikit_digital_health-0.17.16-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (2.4 MB view details)

Uploaded Aug 25, 2026 CPython 3.12manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

scikit_digital_health-0.17.16-cp312-cp312-macosx_26_0_arm64.whl (2.2 MB view details)

Uploaded Aug 25, 2026 CPython 3.12macOS 26.0+ ARM64

scikit_digital_health-0.17.16-cp311-cp311-win_amd64.whl (3.0 MB view details)

Uploaded Aug 25, 2026 CPython 3.11Windows x86-64

scikit_digital_health-0.17.16-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (2.4 MB view details)

Uploaded Aug 25, 2026 CPython 3.11manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

scikit_digital_health-0.17.16-cp311-cp311-macosx_26_0_arm64.whl (2.2 MB view details)

Uploaded Aug 25, 2026 CPython 3.11macOS 26.0+ ARM64

File details

Details for the file scikit_digital_health-0.17.16.tar.gz.

File metadata

Download URL: scikit_digital_health-0.17.16.tar.gz
Upload date: Aug 25, 2026
Size: 8.4 MB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for scikit_digital_health-0.17.16.tar.gz
Algorithm	Hash digest
SHA256	`545e15049c060e2bb7796f9b4107e04a8173f12c328647ff64f9deeaed593444`
MD5	`e20fc6a9b7c9d289941f040c0b486084`
BLAKE2b-256	`fbbe3c787dad08567810c619ee501da8aa1745c356e841ecb05893278327ea95`

Algorithm	Hash digest
SHA256	`cc814d022abe7b2c4c68cfeae4ab8e2c3f162507adc24e296c51c987cae3f147`
MD5	`b4528eeb382150db233e25e98b516136`
BLAKE2b-256	`fe7560467f8f0a58afd44aad44334822d7cdf0c525705e8d29ec0d1b5344235f`

Algorithm	Hash digest
SHA256	`cad4ed019eed70123399ed013ef9eba9afd57967addcada2df152a762c383dcc`
MD5	`1f566dcdff38523880075c33260af4c2`
BLAKE2b-256	`ddacf86c57c530ae6d9643d6653ab4cefae6ba4efa532fe86e358f75fa8f68f3`

Algorithm	Hash digest
SHA256	`1a862698d2487f249bbfb61cd01eac29b04f6ce8c7ec1b0aca4da2d1734cb277`
MD5	`be3f67089369dabc9a34e8736728b94a`
BLAKE2b-256	`290f9b66b956be2d3c6c84c6a5684b5c930e0e27b977c9828ddc18f697161fd7`

Algorithm	Hash digest
SHA256	`7f4cd808d08fca17bddd5e5efdef0d67cba76f5f58816d94fd0c0de14803f949`
MD5	`e9b55532b3ac3545691f6c834c855aab`
BLAKE2b-256	`390f81e6b2172f623a6d3db072ebc78f4efcdd6782edb07901819f574bf8baa0`

Algorithm	Hash digest
SHA256	`11bf75ec381aa67d7b93312e44af7dc7fc63b12db14cb711629127d0f036e0fd`
MD5	`a470ad0aefd86187c5c3e028a2ccfbc9`
BLAKE2b-256	`1b6ec0b3f1248be4b565dc38eef97c012f9a39830338294ee0fcda20d06b2f37`

Algorithm	Hash digest
SHA256	`a885acc99c34d284c387987e30c9835519a6307dbd85cf5cd6a61a44557d1e45`
MD5	`50ab76e34c0162249a4eb989e8325934`
BLAKE2b-256	`a11f6c19c4eb386cb087bcafb75288728e520d29961e000bc58c149dee6877bc`

Algorithm	Hash digest
SHA256	`83d6510425c4528c6fea621060021e874713acc783173bd94fcba3f11ef8bea5`
MD5	`c4aa4eb392aabc8add00b7c6f778fe0d`
BLAKE2b-256	`b573f8965c68d4bb8798a7f88efd65fbd6f1bbc0e63c503d18f65dae6f242d03`

Algorithm	Hash digest
SHA256	`b2fade58bc868d62fb2fd8338ea30925876a9e7c77ecb7e1a90e722ebf523b78`
MD5	`a4f11e0ce404494259b69c83c402e67b`
BLAKE2b-256	`f8f80908bc4c59034db3fda9f5f4d97fa7f08537a57efc249b2cae0fa59ca3c0`

Algorithm	Hash digest
SHA256	`0769e79e1b848bf069c105c4977ec67678ac9fa0d19f49487f745603322bd066`
MD5	`6e1bbac8669d43580a46ed14dac1bd37`
BLAKE2b-256	`eed1cfbd2627e38d7e4e1510d6c56dce59234a587aa60b8b90736ece2a34747e`

Algorithm	Hash digest
SHA256	`953a4f00ab8db667c4bdadae094c97eb0904390eb9560ba1902ad012eb8b9525`
MD5	`b27f030fecadcb59eda531f2b2609c13`
BLAKE2b-256	`18f228ad53f3320d3a0fe3eeb1477449841d9a9644cbb009bbbd4eca4f76bfa2`

Algorithm	Hash digest
SHA256	`c5186280165619252258cc654ce33f62a8627764f3097870728e15e8f5b194f1`
MD5	`913275f5f1fa15f7d91167b3adcc01d9`
BLAKE2b-256	`40c34cd0ae3d39df6841e81db4f1fd16e8e09866f0f056571ff11f4f420f5f86`

Algorithm	Hash digest
SHA256	`c33e3835e931d558ab06bb4d4c85a3c93857ef1b59c686823bf1fc291a1b533f`
MD5	`c6606f650ffaa0fcad861607a03c5e9c`
BLAKE2b-256	`a4771a1ace3b56ed078217df85a589d2c908bc293e875fcc5019c68d1cffab04`

Algorithm	Hash digest
SHA256	`c8d27051d458cece0fc16b120c887f326c22def02670656d19c0bc326ea041cd`
MD5	`4b0197946a7fa50165715cd47d856090`
BLAKE2b-256	`d64c8aa1019cf32ae93c3364dbe677dae26e494332666908c2594189a21aff84`

Algorithm	Hash digest
SHA256	`69278feee9bad62a60dc16edc05f7f85b82e138b80f2fa831033b5e679779c1a`
MD5	`6de169c7e75db22f19a9abaea0bcace8`
BLAKE2b-256	`3c0d583e7d360d17e067c814d3c2630b2b983d93dcebd74ef2b9460dad788acb`

Algorithm	Hash digest
SHA256	`289ebc86b7beb52d91e5892d78b1038fd5cacee5440b7be39d43a957b0f45063`
MD5	`a2ab215a1ce358617b7693df7ce53aab`
BLAKE2b-256	`d76760cf1d0d87323fd99d00a1dded58d64c333cd8e593c758dcc7c82030f1ca`

scikit-digital-health 0.17.16

Availability

Build Requirements

Examples

Citation

Project links

Key dates

1 maintainer

Credits

License

Requires

Provides Extra

Classifiers

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

Release history Release notifications | RSS feed