A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip
In recent years, interest in monitoring Physical Activity (PA) has increased due to its positive effect on health. New technological devices have been proposed for this purpose, mainly focused on sports, which include Machine Learning algorithms to identify the type of PA being performed. However, P...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9269370/ |
| _version_ | 1818853474889105408 |
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| author | Asier Brull Mesanza Sergio Lucas Asier Zubizarreta Itziar Cabanes Eva Portillo Ana Rodriguez-Larrad |
| author_facet | Asier Brull Mesanza Sergio Lucas Asier Zubizarreta Itziar Cabanes Eva Portillo Ana Rodriguez-Larrad |
| author_sort | Asier Brull Mesanza |
| collection | DOAJ |
| description | In recent years, interest in monitoring Physical Activity (PA) has increased due to its positive effect on health. New technological devices have been proposed for this purpose, mainly focused on sports, which include Machine Learning algorithms to identify the type of PA being performed. However, PA monitoring can also provide data useful for assessing the recovery process of people with impaired lower-limbs. In this work, a Machine-Learning based Physical Activity classifier design procedure is proposed, which makes use of the data provided by a Sensorized Tip that can be adapted to different Assistive Devices for Walking (ADW) such as canes or crutches. The procedure is based on three main stages: 1) defining a wide set of potential features to perform the classification; 2) optimizing the number of features by a Random-Forest approach, detecting the most relevant ones to classify five relevant activities (walking at a normal pace, walking fast, standing still, going up stairs and going down stairs); 3) training the ML-based classifiers considering the optimized feature set. A comparative analysis is carried out to evaluate the proposed procedure, using three ML-based classifier (Support Vector Machines, K-Nearest Neighbour and Artificial Neural Networks), demonstrating that the proposed approach can provide very high success rates if proper feature selection is carried out. This work presents four relevant contributions to the PA monitoring area: 1) the approach is focused on people that require ADW, which are not considered in other approaches; 2) an analysis of the features to characterize gait in people that require ADW is carried out; 3) a design procedure to optimize the number of features using a Random-Forest approach is used, avoiding a typical “brute force” procedure; and 4) a comparative analysis is carried out to demonstrate the validity of the approach. |
| first_indexed | 2024-12-19T07:37:24Z |
| format | Article |
| id | doaj.art-9f6737dcd5d7414e97d791c5cda9f042 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:37:24Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9f6737dcd5d7414e97d791c5cda9f0422022-12-21T20:30:33ZengIEEEIEEE Access2169-35362020-01-01821002321003410.1109/ACCESS.2020.30398859269370A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch TipAsier Brull Mesanza0https://orcid.org/0000-0003-4639-970XSergio Lucas1Asier Zubizarreta2https://orcid.org/0000-0001-6049-2308Itziar Cabanes3Eva Portillo4https://orcid.org/0000-0002-1026-3248Ana Rodriguez-Larrad5Department of Automatic Control and Systems Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), Bilbao, SpainDepartment of Automatic Control and Systems Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), Bilbao, SpainDepartment of Automatic Control and Systems Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), Bilbao, SpainDepartment of Automatic Control and Systems Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), Bilbao, SpainDepartment of Automatic Control and Systems Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), Bilbao, SpainDepartment of Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Leioa, SpainIn recent years, interest in monitoring Physical Activity (PA) has increased due to its positive effect on health. New technological devices have been proposed for this purpose, mainly focused on sports, which include Machine Learning algorithms to identify the type of PA being performed. However, PA monitoring can also provide data useful for assessing the recovery process of people with impaired lower-limbs. In this work, a Machine-Learning based Physical Activity classifier design procedure is proposed, which makes use of the data provided by a Sensorized Tip that can be adapted to different Assistive Devices for Walking (ADW) such as canes or crutches. The procedure is based on three main stages: 1) defining a wide set of potential features to perform the classification; 2) optimizing the number of features by a Random-Forest approach, detecting the most relevant ones to classify five relevant activities (walking at a normal pace, walking fast, standing still, going up stairs and going down stairs); 3) training the ML-based classifiers considering the optimized feature set. A comparative analysis is carried out to evaluate the proposed procedure, using three ML-based classifier (Support Vector Machines, K-Nearest Neighbour and Artificial Neural Networks), demonstrating that the proposed approach can provide very high success rates if proper feature selection is carried out. This work presents four relevant contributions to the PA monitoring area: 1) the approach is focused on people that require ADW, which are not considered in other approaches; 2) an analysis of the features to characterize gait in people that require ADW is carried out; 3) a design procedure to optimize the number of features using a Random-Forest approach is used, avoiding a typical “brute force” procedure; and 4) a comparative analysis is carried out to demonstrate the validity of the approach.https://ieeexplore.ieee.org/document/9269370/Instrumented crutchrehabilitationmachine learningphysical activity classificationrandom forestartificial neural network |
| spellingShingle | Asier Brull Mesanza Sergio Lucas Asier Zubizarreta Itziar Cabanes Eva Portillo Ana Rodriguez-Larrad A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip IEEE Access Instrumented crutch rehabilitation machine learning physical activity classification random forest artificial neural network |
| title | A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip |
| title_full | A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip |
| title_fullStr | A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip |
| title_full_unstemmed | A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip |
| title_short | A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip |
| title_sort | machine learning approach to perform physical activity classification using a sensorized crutch tip |
| topic | Instrumented crutch rehabilitation machine learning physical activity classification random forest artificial neural network |
| url | https://ieeexplore.ieee.org/document/9269370/ |
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