Can Trunk Acceleration Differentiate Stroke Patient Gait Patterns Using Time- and Frequency-Domain Features?

This study classified the gait patterns of normal and stroke participants by using time- and frequency-domain features obtained from data provided by an inertial measurement unit sensor placed on the subject’s lower back (L5). Twenty-three participants were included and divided into two groups: heal...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 4; p. 1541
Main Authors Hsu, Wei-Chun, Sugiarto, Tommy, Liao, Ying-Yi, Lin, Yi-Jia, Yang, Fu-Chi, Hueng, Dueng-Yuan, Sun, Chi-Tien, Chou, Kuan-Nien
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2021
Subjects
Accelerometers
Accuracy
Algorithms
clinical gait analysis
Exports
feature selection
Frequency domain analysis
Gait
gait classification
Inertial platforms
Kernels
Machine learning
Model testing
Motion capture
Noise reduction
Older people
Parkinson's disease
Patients
Sensors
Signal to noise ratio
Software
Statistical analysis
Stroke
stroke gait
Support Vector Machine
Support vector machines
Walking
Wearable computers
wearable sensor
United States > US
Sweden
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Summary:This study classified the gait patterns of normal and stroke participants by using time- and frequency-domain features obtained from data provided by an inertial measurement unit sensor placed on the subject’s lower back (L5). Twenty-three participants were included and divided into two groups: healthy group (young and older adults) and stroke group. Time- and frequency-domain features from an accelerometer were extracted, and a feature selection method comprising statistical analysis and signal-to-noise ratio (SNR) calculation was used to reduce the number of features. The features were then used to train four Support Vector Machine (SVM) kernels, and the results were subsequently compared. The quadratic SVM kernel had the highest accuracy (93.46%), as evaluated through cross-validation. Moreover, when different datasets were used on model testing, both the quadratic and cubic kernels showed the highest accuracy (96.55%). These results demonstrated the effectiveness of this study’s classification method in distinguishing between normal and stroke gait patterns, with only using a single sensor placed on the L5.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11041541