Development of a Human Motion Analysis System Based on Sensorized Insoles and Machine Learning Algorithms for Gait Evaluation
Human gait analysis can provide an excellent source for identifying and predicting pathologies and injuries. In this respect, sensorized insoles also have a great potential for extracting gait information. This, combined with mathematical techniques based on machine learning (ML), can potentialize b...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Inventions |
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| Online Access: | https://www.mdpi.com/2411-5134/7/4/98 |
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| author | Diego Henrique Antunes Nascimento Fabrício Anicio Magalhães George Schayer Sabino Renan Alves Resende Maria Lúcia Machado Duarte Claysson Bruno Santos Vimieiro |
| author_facet | Diego Henrique Antunes Nascimento Fabrício Anicio Magalhães George Schayer Sabino Renan Alves Resende Maria Lúcia Machado Duarte Claysson Bruno Santos Vimieiro |
| author_sort | Diego Henrique Antunes Nascimento |
| collection | DOAJ |
| description | Human gait analysis can provide an excellent source for identifying and predicting pathologies and injuries. In this respect, sensorized insoles also have a great potential for extracting gait information. This, combined with mathematical techniques based on machine learning (ML), can potentialize biomechanical analyses. The present study proposes a proof-of-concept of a system based on vertical ground reaction force (vGRF) acquisition with a sensorized insole that uses an ML algorithm to identify different patterns of vGRF and extract biomechanical characteristics that can help during clinical evaluation. The acquired data from the system was clustered by an immunological algorithm (IA) based on vGRF during gait. These clusters underwent a data mining process using the classification and regression tree algorithm (CART), where the main characteristics of each group were extracted, and some rules for gait classification were created. As a result, the system proposed was able to collect and process the biomechanical behavior of gait. After the application of IA and CART algorithms, six groups were found. The characteristics of each of these groups were extracted and verified the capability of the system to collect and process the biomechanical behavior of gait, offering verification points that can help focus during a clinical evaluation. |
| first_indexed | 2024-03-09T03:32:51Z |
| format | Article |
| id | doaj.art-d6a6b36e2c004c5db50db465c09031fd |
| institution | Directory Open Access Journal |
| issn | 2411-5134 |
| language | English |
| last_indexed | 2024-03-09T03:32:51Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Inventions |
| spelling | doaj.art-d6a6b36e2c004c5db50db465c09031fd2023-12-03T14:53:31ZengMDPI AGInventions2411-51342022-10-01749810.3390/inventions7040098Development of a Human Motion Analysis System Based on Sensorized Insoles and Machine Learning Algorithms for Gait EvaluationDiego Henrique Antunes Nascimento0Fabrício Anicio Magalhães1George Schayer Sabino2Renan Alves Resende3Maria Lúcia Machado Duarte4Claysson Bruno Santos Vimieiro5Bioengineering Laboratory (LABBIO), Graduate Program in Mechanical Engineering (PPGMEC), Universidade Federal de Minas Gerais (UFMG), Belo Horizonte 31270-901, MG, BrazilDepartment of Biomechanics, College of Education, Health, and Human Sciences, University of Nebraska at Omaha, Omaha, NE 68182, USAGraduate Program in Rehabilitation Sciences, School of Physical Education, Physical Therapy and Occupational Therapy (EEFFTO), Universidade Federal de Minas Gerais (UFMG), Belo Horizonte 31270-901, MG, BrazilGraduate Program in Rehabilitation Sciences, School of Physical Education, Physical Therapy and Occupational Therapy (EEFFTO), Universidade Federal de Minas Gerais (UFMG), Belo Horizonte 31270-901, MG, BrazilBioengineering Laboratory (LABBIO), Graduate Program in Mechanical Engineering (PPGMEC), Universidade Federal de Minas Gerais (UFMG), Belo Horizonte 31270-901, MG, BrazilBioengineering Laboratory (LABBIO), Graduate Program in Mechanical Engineering (PPGMEC), Universidade Federal de Minas Gerais (UFMG), Belo Horizonte 31270-901, MG, BrazilHuman gait analysis can provide an excellent source for identifying and predicting pathologies and injuries. In this respect, sensorized insoles also have a great potential for extracting gait information. This, combined with mathematical techniques based on machine learning (ML), can potentialize biomechanical analyses. The present study proposes a proof-of-concept of a system based on vertical ground reaction force (vGRF) acquisition with a sensorized insole that uses an ML algorithm to identify different patterns of vGRF and extract biomechanical characteristics that can help during clinical evaluation. The acquired data from the system was clustered by an immunological algorithm (IA) based on vGRF during gait. These clusters underwent a data mining process using the classification and regression tree algorithm (CART), where the main characteristics of each group were extracted, and some rules for gait classification were created. As a result, the system proposed was able to collect and process the biomechanical behavior of gait. After the application of IA and CART algorithms, six groups were found. The characteristics of each of these groups were extracted and verified the capability of the system to collect and process the biomechanical behavior of gait, offering verification points that can help focus during a clinical evaluation.https://www.mdpi.com/2411-5134/7/4/98biomechanics on gaitdata mininggait analysismachine learningsmart insole |
| spellingShingle | Diego Henrique Antunes Nascimento Fabrício Anicio Magalhães George Schayer Sabino Renan Alves Resende Maria Lúcia Machado Duarte Claysson Bruno Santos Vimieiro Development of a Human Motion Analysis System Based on Sensorized Insoles and Machine Learning Algorithms for Gait Evaluation Inventions biomechanics on gait data mining gait analysis machine learning smart insole |
| title | Development of a Human Motion Analysis System Based on Sensorized Insoles and Machine Learning Algorithms for Gait Evaluation |
| title_full | Development of a Human Motion Analysis System Based on Sensorized Insoles and Machine Learning Algorithms for Gait Evaluation |
| title_fullStr | Development of a Human Motion Analysis System Based on Sensorized Insoles and Machine Learning Algorithms for Gait Evaluation |
| title_full_unstemmed | Development of a Human Motion Analysis System Based on Sensorized Insoles and Machine Learning Algorithms for Gait Evaluation |
| title_short | Development of a Human Motion Analysis System Based on Sensorized Insoles and Machine Learning Algorithms for Gait Evaluation |
| title_sort | development of a human motion analysis system based on sensorized insoles and machine learning algorithms for gait evaluation |
| topic | biomechanics on gait data mining gait analysis machine learning smart insole |
| url | https://www.mdpi.com/2411-5134/7/4/98 |
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