Published on in Vol 5, No 12 (2017): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9177, first published .
Addendum of: Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

Addendum of: Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

Addendum of: Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

Journals

  1. Yao L, Yang W, Huang W. A data augmentation method for human action recognition using dense joint motion images. Applied Soft Computing 2020;97:106713 View
  2. Zelman S, Dow M, Tabashum T, Xiao T, Albert M. Accelerometer-Based Automated Counting of Ten Exercises without Exercise-Specific Training or Tuning. Journal of Healthcare Engineering 2020;2020:1 View
  3. Maldonado-Contreras J, Bhakta K, Camargo J, Kunapuli P, Young A. User- and Speed-Independent Slope Estimation for Lower-Extremity Wearable Robots. Annals of Biomedical Engineering 2024;52(3):487 View
  4. Zhang J, Li Z, Liu Y, Li J, Qiu H, Li M, Hou G, Zhou Z. An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design. Journal of Medical Internet Research 2024;26:e56750 View
  5. Arora V, Mishra S, Joshi A, Singhal M, Sahoo J. Aiding Clinical Decision-Making at the Individual and Community Level Using Mobile Sensor Data: Study Protocol for an Experimental Design. Cureus 2024 View