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Gait Disorder Detection and Classification Method Using Inertia Measurement Unit for Augmented Feedback Training in Wearable Devices
| Content Provider | MDPI |
|---|---|
| Author | Kim, Hyeon Jong Kim, Ji-Won Ko, Junghyuk |
| Copyright Year | 2021 |
| Description | Parkinson’s disease (PD) is a common neurodegenerative disease, one of the symptoms of which is a gait disorder, which decreases gait speed and cadence. Recently, augmented feedback training has been considered to achieve effective physical rehabilitation. Therefore, we have devised a numerical modeling process and algorithm for gait detection and classification (GDC) that actively utilizes augmented feedback training. The numerical model converted each joint angle into a magnitude of acceleration (MoA) and a Z-axis angular velocity (ZAV) parameter. Subsequently, we confirmed the validity of both the GDC numerical modeling and algorithm. As a result, a higher gait detection and classification rate (GDCR) could be observed at a higher gait speed and lower acceleration threshold (AT) and gyroscopic threshold (GT). However, the pattern of the GDCR was ambiguous if the patient was affected by a gait disorder compared to a normal user. To utilize the relationships between the GDCR, AT, GT, and gait speed, we controlled the GDCR by using AT and GT as inputs, which we found to be a reasonable methodology. Moreover, the GDC algorithm could distinguish between normal people and people who suffered from gait disorders. Consequently, the GDC method could be used for rehabilitation and gait evaluation. |
| Starting Page | 7676 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21227676 |
| Journal | Sensors |
| Issue Number | 22 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-18 |
| Access Restriction | Open |
| Subject Keyword | Sensors Parkinson's Disease Gait Disorder Augmented Feedback Training Gait Detection Gait Classification Wearable Device |
| Content Type | Text |
| Resource Type | Article |
