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

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

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, re...

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Bibliografiska uppgifter
Huvudupphovsmän: Xiaolei Lu, Chenye Qiao, Hujun Wang, Yingqi Li, Jingxuan Wang, Congxiao Wang, Yingpeng Wang, Shuyan Qie
Materialtyp: Artikel
Språk:English
Publicerad: MDPI AG 2024-11-01
Serie:Sensors
Ämnen:
sensors
isokinetic dynamometry
gait deviation index
machine learning
stroke
Länkar:https://www.mdpi.com/1424-8220/24/22/7258

Internet

https://www.mdpi.com/1424-8220/24/22/7258

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