Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair
Many modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are not aware of their postures and do not take advanta...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/1/400 |
| _version_ | 1797497530037043200 |
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| author | Ghazal Farhani Yue Zhou Patrick Danielson Ana Luisa Trejos |
| author_facet | Ghazal Farhani Yue Zhou Patrick Danielson Ana Luisa Trejos |
| author_sort | Ghazal Farhani |
| collection | DOAJ |
| description | Many modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are not aware of their postures and do not take advantage of the increased range of motion offered by the dynamic chairs. Building a system that identifies users’ postures in real time, as well as forecasts the next few postures, can bring awareness to the sitting behavior of each user. In this study, machine learning algorithms have been implemented to automatically classify users’ postures and forecast their next motions. The random forest, gradient decision tree, and support vector machine algorithms were used to classify postures. The evaluation of the trained classifiers indicated that they could successfully identify users’ postures with an accuracy above 90%. The algorithm can provide users with an accurate report of their sitting habits. A 1D-convolutional-LSTM network has also been implemented to forecast users’ future postures based on their previous motions, the model can forecast a user’s motions with high accuracy (97%). The ability of the algorithm to forecast future postures could be used to suggest alternative postures as needed. |
| first_indexed | 2024-03-10T03:20:31Z |
| format | Article |
| id | doaj.art-c9b610f19f624f5e916d2ea69fd06286 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:20:31Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c9b610f19f624f5e916d2ea69fd062862023-11-23T12:21:39ZengMDPI AGSensors1424-82202022-01-0122140010.3390/s22010400Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic ChairGhazal Farhani0Yue Zhou1Patrick Danielson2Ana Luisa Trejos3Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, CanadaSchool of Biomedical Engineering (BME), Western University, London, ON N6A 3K7, CanadaFormid, London, ON N0L 1G0, CanadaDepartment of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, CanadaMany modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are not aware of their postures and do not take advantage of the increased range of motion offered by the dynamic chairs. Building a system that identifies users’ postures in real time, as well as forecasts the next few postures, can bring awareness to the sitting behavior of each user. In this study, machine learning algorithms have been implemented to automatically classify users’ postures and forecast their next motions. The random forest, gradient decision tree, and support vector machine algorithms were used to classify postures. The evaluation of the trained classifiers indicated that they could successfully identify users’ postures with an accuracy above 90%. The algorithm can provide users with an accurate report of their sitting habits. A 1D-convolutional-LSTM network has also been implemented to forecast users’ future postures based on their previous motions, the model can forecast a user’s motions with high accuracy (97%). The ability of the algorithm to forecast future postures could be used to suggest alternative postures as needed.https://www.mdpi.com/1424-8220/22/1/400dynamic chairsposture classificationmachine learning applicationlong short-term memory (LSTM)1D-CNN-LSTM |
| spellingShingle | Ghazal Farhani Yue Zhou Patrick Danielson Ana Luisa Trejos Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair Sensors dynamic chairs posture classification machine learning application long short-term memory (LSTM) 1D-CNN-LSTM |
| title | Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair |
| title_full | Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair |
| title_fullStr | Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair |
| title_full_unstemmed | Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair |
| title_short | Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair |
| title_sort | implementing machine learning algorithms to classify postures and forecast motions when using a dynamic chair |
| topic | dynamic chairs posture classification machine learning application long short-term memory (LSTM) 1D-CNN-LSTM |
| url | https://www.mdpi.com/1424-8220/22/1/400 |
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