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

Wearables and MHealth Viewpoints (68) Tools, Programs and Algorithms (207) mHealth for Data Collection and Research (770) Evaluation and Research Methodology for mHealth (174) Wearable Devices and Sensors (729)

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 (4) Tweetations (9) 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
  2. Abbasian M, Khatibi E, Azimi I, Rahmani A. PHAS: An End-to-End, Open-Source, and Portable Healthcare Analytics Stack. Procedia Computer Science 2023;220:511 View
  3. Lin W, Karahanoglu F, Psaltos D, Adamowicz L, Santamaria M, Cai X, Demanuele C, Di J. Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?. Sensors 2023;23(20):8542 View
  4. Lin W, Karahanoglu F, Demanuele C, Khan S, Cai X, Santamaria M, Di J, Adamowicz L. SciKit digital health package for accelerometry-measured physical activity: comparisons to existing solutions and investigations of age effects in healthy adults. Frontiers in Digital Health 2023;5 View