Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study.

Journal: Sensors (Basel, Switzerland)
PMID: 39599035

Abstract

BACKGROUND: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and challenging to interpret in clinical practice, requiring significant time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry, utilizing sophisticated sensors, is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored.

Authors

  • Xiaolei Lu
  • Chenye Qiao
    Beijing Rehabilitation Medicine, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China.
  • Hujun Wang
    Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China.
  • Yingqi Li
    Imaging Department, Shenzhen Bao'an District Songgang People's Hospital, Shenzhen, Guangdong, China.
  • Jingxuan Wang
    College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, P.R.China.
  • Congxiao Wang
    Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China.
  • Yingpeng Wang
    Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China.
  • Shuyan Qie
    Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China.

Keywords

External Resources

Popular Topics
Recent Journals