Explaining deep learning models for age-related gait classification based on acceleration time series.

Journal: Computers in biology and medicine
PMID: 39536383

Abstract

BACKGROUND: Gait analysis holds significant importance in monitoring daily health, particularly among older adults. Advancements in sensor technology enable the capture of movement in real-life environments and generate big data. Machine learning, notably deep learning (DL), shows promise to use these big data in gait analysis. However, the inherent black-box nature of these models poses challenges for their clinical application. This study aims to enhance transparency in DL-based gait classification for aged-related gait patterns using Explainable Artificial Intelligence, such as SHapley Additive exPlanations (SHAP).

Authors

  • Xiaoping Zheng
    Department of Urology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China.
  • Egbert Otten
    University of Groningen, University Medical Center Groningen, Department of Human Movement Sciences, Groningen, the Netherlands.
  • Michiel F Reneman
    University of Groningen, University Medical Center Groningen, Department of Rehabilitation Medicine, Groningen, the Netherlands. Electronic address: m.f.reneman@umcg.nl.
  • Claudine Jc Lamoth
    University of Groningen, University Medical Center Groningen, Department of Human Movement Sciences, Groningen, the Netherlands.

Keywords

External Resources

Popular Topics
Recent Journals