Skip to main content

No project description provided

Project description

Dataset for KCC2020: Tutorial on Human Activity Recognition

Install

pip install kcc2020-tutorial-HAR-dataset

How to use

  • This dataset is for the KCC2020 tutorial on human activity recognition.
  • This dataset is originally from UCI's HAPT (Human Activities and Postural Transitions) an modified for KCC 2020 Sensing tutorial.
  • You can download original dataset from the HAPT website (for details of the dataset, see HAPT dataset website).

load_all

  • To download dataset of entire users, use 'load_all().'

  • For example:

from KCC2020_HAR_dataset import load_all

entire_datase = load_all()
  • It automatically removes any part of data that has no labels.

  • To obtain data with the removal, please use 'load_all(remove_no_lavels = False).'

load_by_user

  • To download dataset of a specific user, use 'load_by_user(uid).'

  • It returns pandas's DataFrame that contains the dataset of the specific user with given uid.

  • There is 30 users; it returns 'None' if uid is greater than 30.

  • For example:

from KCC2020_HAR_dataset import load_by_user

user1_datasett = load_by_user(1)
  • It automatically removes any part of data that has no labels.

  • To obtain data with the removal, please use 'load_by_user(uid, remove_no_lavels = False).'

Project details


Download files

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

Source Distribution

kcc2020-tutorial-HAR-dataset-0.1.4.tar.gz (18.8 MB view details)

Uploaded Source

File details

Details for the file kcc2020-tutorial-HAR-dataset-0.1.4.tar.gz.

File metadata

  • Download URL: kcc2020-tutorial-HAR-dataset-0.1.4.tar.gz
  • Upload date:
  • Size: 18.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.3.1 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.7.7

File hashes

Hashes for kcc2020-tutorial-HAR-dataset-0.1.4.tar.gz
Algorithm Hash digest
SHA256 0e4ae3bc55377d958703ac737337494393091cad9078cf1971221a1705c44d9f
MD5 3bc327d7597d9999a4f749dda8b0f28f
BLAKE2b-256 e6cd397dfb2a5cb18991394e0b52d3153646367584253f3a31fe0b36c976b657

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page