Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors
Neural coding of gait intent and continuous gait kinematics have advanced brain computer interface (BCI) technology for detection and predicting human upright walking movement. However, the dynamics of cortical involvement in upright walking and upright standing has not been clearly understood espec...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
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IntechOpen
2020
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| Acceso en línea: | https://www.intechopen.com/chapters/68413 |
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| _version_ | 1879164703868977152 |
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| author | Nutakki, Chaitanya Bodda, Sandeep Diwakar, Shyam |
| author_browse | Bodda, Sandeep Diwakar, Shyam Nutakki, Chaitanya |
| author_facet | Nutakki, Chaitanya Bodda, Sandeep Diwakar, Shyam |
| author_sort | Nutakki, Chaitanya |
| building | IntechOpen |
| collection | Acceso abierto |
| description | Neural coding of gait intent and continuous gait kinematics have advanced brain computer interface (BCI) technology for detection and predicting human upright walking movement. However, the dynamics of cortical involvement in upright walking and upright standing has not been clearly understood especially with the focus of off-laboratory assessments. In this study, wearable low-cost mobile phone accelerometers were used to extract position and velocity at 12 joints during walking and the cortical changes involved during gait phases of walking were explored using non-invasive electroencephalogram (EEG). Extracted gait data included, accelerometer values proximal to brachium of arm, antecubitis, carpus, coxal, femur and tarsus by considering physical parameters including height, weight and stride length. Including EEG data as features, the spectral and temporal features were used to classify and predict the swing and stance instances for healthy subjects. While focusing on stance and swing classification in healthy subjects, this chapter relates to gait features that help discriminate walking movement and its neurophysiological counterparts. With promising initial results, further exploration of gait may help change detection of movement neurological conditions in regions where specialists and clinical facilities may not be at par. |
| doi_str_mv | 10.5772/intechopen.88465 |
| first_indexed | 2026-10-15T21:38:33Z |
| format | Book Chapter |
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<dc:title>Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors</dc:title>
<dc:creator>Chaitanya Nutakki</dc:creator>
<dc:creator>Sandeep Bodda</dc:creator>
<dc:creator>Shyam Diwakar</dc:creator>
<dc:subject>Life Sciences</dc:subject>
<dc:description>Neural coding of gait intent and continuous gait kinematics have advanced brain computer interface (BCI) technology for detection and predicting human upright walking movement. However, the dynamics of cortical involvement in upright walking and upright standing has not been clearly understood especially with the focus of off-laboratory assessments. In this study, wearable low-cost mobile phone accelerometers were used to extract position and velocity at 12 joints during walking and the cortical changes involved during gait phases of walking were explored using non-invasive electroencephalogram (EEG). Extracted gait data included, accelerometer values proximal to brachium of arm, antecubitis, carpus, coxal, femur and tarsus by considering physical parameters including height, weight and stride length. Including EEG data as features, the spectral and temporal features were used to classify and predict the swing and stance instances for healthy subjects. While focusing on stance and swing classification in healthy subjects, this chapter relates to gait features that help discriminate walking movement and its neurophysiological counterparts. With promising initial results, further exploration of gait may help change detection of movement neurological conditions in regions where specialists and clinical facilities may not be at par.</dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2020-09-09</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/68413</dc:identifier>
<dc:identifier>doi:10.5772/intechopen.88465</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-1-78984-113-8</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/8851 ; Advances in Neural Signal Processing</dc:source>
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| id | intech-68413 |
| institution | IntechOpen |
| isbn | 978-1-78984-113-8 |
| language | English |
| last_indexed | 2026-10-15T21:38:33Z |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | oai_dc |
| spelling | intech-684132020-09-09 Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors Chaitanya Nutakki Sandeep Bodda Shyam Diwakar Life Sciences Neural coding of gait intent and continuous gait kinematics have advanced brain computer interface (BCI) technology for detection and predicting human upright walking movement. However, the dynamics of cortical involvement in upright walking and upright standing has not been clearly understood especially with the focus of off-laboratory assessments. In this study, wearable low-cost mobile phone accelerometers were used to extract position and velocity at 12 joints during walking and the cortical changes involved during gait phases of walking were explored using non-invasive electroencephalogram (EEG). Extracted gait data included, accelerometer values proximal to brachium of arm, antecubitis, carpus, coxal, femur and tarsus by considering physical parameters including height, weight and stride length. Including EEG data as features, the spectral and temporal features were used to classify and predict the swing and stance instances for healthy subjects. While focusing on stance and swing classification in healthy subjects, this chapter relates to gait features that help discriminate walking movement and its neurophysiological counterparts. With promising initial results, further exploration of gait may help change detection of movement neurological conditions in regions where specialists and clinical facilities may not be at par. IntechOpen 2020-09-09 Chapter, Part Of Book https://www.intechopen.com/chapters/68413 doi:10.5772/intechopen.88465 en ISBN:978-1-78984-113-8 https://creativecommons.org/licenses/by/3.0/ https://www.intechopen.com/books/8851 ; Advances in Neural Signal Processing |
| spellingShingle | Life Sciences Nutakki, Chaitanya Bodda, Sandeep Diwakar, Shyam Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors |
| title | Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors |
| title_full | Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors |
| title_fullStr | Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors |
| title_full_unstemmed | Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors |
| title_short | Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors |
| title_sort | correlations of gait phase kinematics and cortical eeg modelling human gait with data from sensors |
| topic | Life Sciences |
| url | https://www.intechopen.com/chapters/68413 |
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