Principal Characteristics of Affected and Unaffected Side Trunk Movement and Gait Event Parameters during Hemiplegic Stroke Gait with IMU Sensor
| Autor: | Boncho Ku, Joong Il Kim, Sangkwan Lee, Jeong-Woo Seo, Kahye Kim, SeulGee Kim, Jaeuk U. Kim |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
030506 rehabilitation medicine.medical_specialty Multivariate statistics Letter principal component analysis Hemiplegia lcsh:Chemical technology Biochemistry gait event Analytical Chemistry Pelvis 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Gait (human) Inertial measurement unit medicine Humans lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Stroke Gait Disorders Neurologic Event (probability theory) Aged Monitoring Physiologic IMU sensor business.industry Movement (music) hemiplegic gait Torso Middle Aged medicine.disease Trunk stroke Atomic and Molecular Physics and Optics Biomechanical Phenomena Principal component analysis Female 0305 other medical science business Gait Analysis 030217 neurology & neurosurgery |
| Zdroj: | Sensors, Vol 20, Iss 7338, p 7338 (2020) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | This study describe the characteristics of hemiplegic stroke gait with principal component analysis (PCA) of trunk movement (TM) and gait event (GE) parameters by the inertial measurement unit (IMU) sensors: (1) Background: This process can determine dominant variables through multivariate examination to identify the affected, unaffected, and healthy lower-limb sides; (2) Methods: The study monitored forty patients with stroke and twenty-eight healthy individuals comprising the control group for comparison. The IMU sensors were attached to each subject while performing a 6 m walking test. Sixteen variables extracted from the measured data were divided into 7 GE and 9 TM variables explaining pelvis tilt, oblique, and rotation. (3) Results: The tilt range variables of the trunk movement on the affected and unaffected sides were lower than those of the healthy side; this showed between-group differences in various GE variables. For the healthy and affected sides, 80% of variances were explained with 2 or 3 PCs involving only a few dominant variables; and (4) Conclusions: The difference between each side leg should be considered during the development of a diagnosis method. This research can be utilized to develop functional assessment tools for personalized treatment and to design appropriate training protocols. |
| Databáze: | OpenAIRE |
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