This paper is in the following e-collection/theme issue:

Wearables and MHealth Viewpoints (62) Tools, Programs and Algorithms (181) mHealth for Data Collection and Research (675) Evaluation and Research Methodology for mHealth (160) Wearable Devices and Sensors (657)

Published on in Vol 10 , No 4 (2022) :April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36762, first published .
SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing

SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing

SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing

Authors of this article:

Lukas Adamowicz 1 Author Orcid Image ;   Yiorgos Christakis 1 Author Orcid Image ;   Matthew D Czech 1 Author Orcid Image ;   Tomasz Adamusiak 1 Author Orcid Image
Article Authors Cited by (1) Tweetations (10) Metrics

Journals

  1. Brand Y, Schwartz D, Gazit E, Buchman A, Gilad-Bachrach R, Hausdorff J. Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson’s Disease. Sensors 2022;22(18):7094 View