Abstract
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.
Highlights
Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, National Defense Medical Center, Taipei 11490, Taiwan
To the best of our knowledge, this study is the first to classify normal and stroke gait patterns by using features obtained from an inertial measurement unit (IMU) sensor placed on the subject’s lower back
If we compared with more recent published studies that used a wearable device to classify different gait patterns, we found that our result still outperformed the best result from Caramia et al, in 2018, which produced 96%
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). 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. Especially ones equipped with accelerometers and inertial measurement unit (IMU) sensors, have been widely used in gait analysis or physical activity monitoring [1,2,3,4]. The bigger amount of data collected, combined with specific feature extraction process, will lead to possible application in clinical gait analysis, such as early detection of gait movement disorder
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
