Tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. The ability to accurately perform human gait evaluation is critical for orthopedic foot and ankle surgeons in tracking the recovery process of their patients. The assessment of gait in an objective and accurate manner can lead to improvement...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/76314 |
| _version_ | 1848763666179031040 |
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| author | Bidabadi, Shiva Sharif Tan, Tele Murray, Iain Lee, G. |
| author_facet | Bidabadi, Shiva Sharif Tan, Tele Murray, Iain Lee, G. |
| author_sort | Bidabadi, Shiva Sharif |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. The ability to accurately perform human gait evaluation is critical for orthopedic foot and ankle surgeons in tracking the recovery process of their patients. The assessment of gait in an objective and accurate manner can lead to improvement in diagnoses, treatments, and recovery. Currently, visual inspection is the most common clinical method for evaluating the gait, but this method can be subjective and inaccurate. The aim of this study is to evaluate the foot drop condition in an accurate and clinically applicable manner. The gait data were collected from 56 patients suffering from foot drop with L5 origin gathered via a system based on inertial measurement unit sensors at different stages of surgical treatment. Various machine learning (ML) algorithms were applied to categorize the data into specific groups associated with the recovery stages. The results revealed that the random forest algorithm performed best out of the selected ML algorithms, with an overall 84.89% classification accuracy and 0.3785 mean absolute error for regression. |
| first_indexed | 2025-11-14T11:07:05Z |
| format | Journal Article |
| id | curtin-20.500.11937-76314 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:07:05Z |
| publishDate | 2019 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-763142021-01-05T08:07:08Z Tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques Bidabadi, Shiva Sharif Tan, Tele Murray, Iain Lee, G. Science & Technology Physical Sciences Technology Chemistry, Analytical Electrochemistry Instruments & Instrumentation Chemistry foot drop gait classification machine learning inertial measurement unit FUNCTIONAL AMBULATION VALIDATION STRENGTH VALIDITY © 2019 by the authors. Licensee MDPI, Basel, Switzerland. The ability to accurately perform human gait evaluation is critical for orthopedic foot and ankle surgeons in tracking the recovery process of their patients. The assessment of gait in an objective and accurate manner can lead to improvement in diagnoses, treatments, and recovery. Currently, visual inspection is the most common clinical method for evaluating the gait, but this method can be subjective and inaccurate. The aim of this study is to evaluate the foot drop condition in an accurate and clinically applicable manner. The gait data were collected from 56 patients suffering from foot drop with L5 origin gathered via a system based on inertial measurement unit sensors at different stages of surgical treatment. Various machine learning (ML) algorithms were applied to categorize the data into specific groups associated with the recovery stages. The results revealed that the random forest algorithm performed best out of the selected ML algorithms, with an overall 84.89% classification accuracy and 0.3785 mean absolute error for regression. 2019 Journal Article http://hdl.handle.net/20.500.11937/76314 10.3390/s19112542 English http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Physical Sciences Technology Chemistry, Analytical Electrochemistry Instruments & Instrumentation Chemistry foot drop gait classification machine learning inertial measurement unit FUNCTIONAL AMBULATION VALIDATION STRENGTH VALIDITY Bidabadi, Shiva Sharif Tan, Tele Murray, Iain Lee, G. Tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques |
| title | Tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques |
| title_full | Tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques |
| title_fullStr | Tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques |
| title_full_unstemmed | Tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques |
| title_short | Tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques |
| title_sort | tracking foot drop recovery following lumbar-spine surgery, applying multiclass gait classification using machine learning techniques |
| topic | Science & Technology Physical Sciences Technology Chemistry, Analytical Electrochemistry Instruments & Instrumentation Chemistry foot drop gait classification machine learning inertial measurement unit FUNCTIONAL AMBULATION VALIDATION STRENGTH VALIDITY |
| url | http://hdl.handle.net/20.500.11937/76314 |