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

mHealth for Data Collection and Research (954) Wearable Devices and Sensors (887) Fitness Trackers and Smart Pedometers/Accelerometers (560) Portable and Mobile Technologies for Rehabilitation (151) mHealth for Wellness, Behavior Change and Prevention (2869) Mobile Health (mhealth) (2939)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24194, first published .
Measurement of Human Walking Movements by Using a Mobile Health App: Motion Sensor Data Analysis

Measurement of Human Walking Movements by Using a Mobile Health App: Motion Sensor Data Analysis

Measurement of Human Walking Movements by Using a Mobile Health App: Motion Sensor Data Analysis

Authors of this article:

Sungchul Lee1 Author Orcid Image ;   Ryan M Walker2 Author Orcid Image ;   Yoohwan Kim2 Author Orcid Image ;   Hyunhwa Lee3 Author Orcid Image
Article Authors Cited by (4) Tweetations (15) Metrics

Journals

  1. Cook D, Schmitter-Edgecombe M. Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores. IEEE Access 2021;9:65033 View
  2. Lee S, Hwang E, Kim Y, Demir F, Lee H, Mosher J, Jang E, Lim K. Mobile Health App for Adolescents: Motion Sensor Data and Deep Learning Technique to Examine the Relationship between Obesity and Walking Patterns. Applied Sciences 2022;12(2):850 View
  3. Soulard J, Vaillant J, Baillet A, Gaudin P, Vuillerme N. Gait and Axial Spondyloarthritis: Comparative Gait Analysis Study Using Foot-Worn Inertial Sensors. JMIR mHealth and uHealth 2021;9(11):e27087 View
  4. Degbey G, Hwang E, Park J, Lee S. Deep Learning-Based Obesity Identification System for Young Adults Using Smartphone Inertial Measurements. International Journal of Environmental Research and Public Health 2024;21(9):1178 View