Using machine learning algorithms for grasp strength recognition in rehabilitation planning
The augmentation of individuals' quality of life, particularly those with disabilities, can be achieved through state-of-the-art artificial intelligence solutions. Machine learning algorithms, known for their ability to acquiring knowledge and identify significant characteristics from diverse d...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023007879 |
| _version_ | 1797247743950848000 |
|---|---|
| author | Tanin Boka Arshia Eskandari S. Ali A. Moosavian Mahkame Sharbatdar |
| author_facet | Tanin Boka Arshia Eskandari S. Ali A. Moosavian Mahkame Sharbatdar |
| author_sort | Tanin Boka |
| collection | DOAJ |
| description | The augmentation of individuals' quality of life, particularly those with disabilities, can be achieved through state-of-the-art artificial intelligence solutions. Machine learning algorithms, known for their ability to acquiring knowledge and identify significant characteristics from diverse datasets, play a crucial role. In this investigation, we focused on classifying various weights commonly encountered in daily activities based on electromyography (EMG) readings, using multiple distinct machine learning algorithms. This endeavor involved collection of substantial data from a substantial cohort, wherein participants assumed distinct arm configurations while manipulating three various objects (specifically, a pen, a bottle, and a weighty object) or no object at all. The sample encompassed 50 physically capable and healthy participants, with an equal distribution of 25 males and 25 females. The muscular activity was measured utilizing the MYO armband, an advanced eight-channel EMG device positioned on the forearm. After the preprocessing of this data, several machine learning algorithms has been employed to analyze the dataset. Notably, the outcomes demonstrate that the K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT) algorithms emerge as the optimal methodologies for grip strength estimation, achieving impressive accuracy rates of 99.23 %, 99.08 %, and 98.62 %, respectively. The experimental data, and supplementary materials are available at https://github.com/arshiaeskandari/EMG-Dataset. |
| first_indexed | 2024-03-08T22:14:14Z |
| format | Article |
| id | doaj.art-7062f1d36c214613a8ee2c329ea25a23 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-24T20:03:33Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-7062f1d36c214613a8ee2c329ea25a232024-03-24T07:00:08ZengElsevierResults in Engineering2590-12302024-03-0121101660Using machine learning algorithms for grasp strength recognition in rehabilitation planningTanin Boka0Arshia Eskandari1S. Ali A. Moosavian2Mahkame Sharbatdar3Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, IranDepartment of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, IranDepartment of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, IranCorresponding author.; Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, IranThe augmentation of individuals' quality of life, particularly those with disabilities, can be achieved through state-of-the-art artificial intelligence solutions. Machine learning algorithms, known for their ability to acquiring knowledge and identify significant characteristics from diverse datasets, play a crucial role. In this investigation, we focused on classifying various weights commonly encountered in daily activities based on electromyography (EMG) readings, using multiple distinct machine learning algorithms. This endeavor involved collection of substantial data from a substantial cohort, wherein participants assumed distinct arm configurations while manipulating three various objects (specifically, a pen, a bottle, and a weighty object) or no object at all. The sample encompassed 50 physically capable and healthy participants, with an equal distribution of 25 males and 25 females. The muscular activity was measured utilizing the MYO armband, an advanced eight-channel EMG device positioned on the forearm. After the preprocessing of this data, several machine learning algorithms has been employed to analyze the dataset. Notably, the outcomes demonstrate that the K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT) algorithms emerge as the optimal methodologies for grip strength estimation, achieving impressive accuracy rates of 99.23 %, 99.08 %, and 98.62 %, respectively. The experimental data, and supplementary materials are available at https://github.com/arshiaeskandari/EMG-Dataset.http://www.sciencedirect.com/science/article/pii/S2590123023007879Electromyography signals (EMG)ClassificationGrip strength estimationMachine learning |
| spellingShingle | Tanin Boka Arshia Eskandari S. Ali A. Moosavian Mahkame Sharbatdar Using machine learning algorithms for grasp strength recognition in rehabilitation planning Results in Engineering Electromyography signals (EMG) Classification Grip strength estimation Machine learning |
| title | Using machine learning algorithms for grasp strength recognition in rehabilitation planning |
| title_full | Using machine learning algorithms for grasp strength recognition in rehabilitation planning |
| title_fullStr | Using machine learning algorithms for grasp strength recognition in rehabilitation planning |
| title_full_unstemmed | Using machine learning algorithms for grasp strength recognition in rehabilitation planning |
| title_short | Using machine learning algorithms for grasp strength recognition in rehabilitation planning |
| title_sort | using machine learning algorithms for grasp strength recognition in rehabilitation planning |
| topic | Electromyography signals (EMG) Classification Grip strength estimation Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023007879 |
| work_keys_str_mv | AT taninboka usingmachinelearningalgorithmsforgraspstrengthrecognitioninrehabilitationplanning AT arshiaeskandari usingmachinelearningalgorithmsforgraspstrengthrecognitioninrehabilitationplanning AT saliamoosavian usingmachinelearningalgorithmsforgraspstrengthrecognitioninrehabilitationplanning AT mahkamesharbatdar usingmachinelearningalgorithmsforgraspstrengthrecognitioninrehabilitationplanning |