Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems
The rate of development of robotic technologies has been meteoric, as a result of compounded advancements in hardware and software. Amongst these robotic technologies are active exoskeletons and orthoses, used in the assistive and rehabilitative fields. Artificial intelligence techniques are increas...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9513306/ |
| _version_ | 1830093637335121920 |
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| author | Rania Kolaghassi Mohamad Kenan Al-Hares Konstantinos Sirlantzis |
| author_facet | Rania Kolaghassi Mohamad Kenan Al-Hares Konstantinos Sirlantzis |
| author_sort | Rania Kolaghassi |
| collection | DOAJ |
| description | The rate of development of robotic technologies has been meteoric, as a result of compounded advancements in hardware and software. Amongst these robotic technologies are active exoskeletons and orthoses, used in the assistive and rehabilitative fields. Artificial intelligence techniques are increasingly being utilised in gait analysis and prediction. This review paper systematically explores the current use of intelligent algorithms in gait analysis for robotic control, specifically the control of active lower limb exoskeletons and orthoses. Two databases, IEEE and Scopus, were screened for papers published between 1989 to May 2020. 41 papers met the eligibility criteria and were included in this review. 66.7% of the identified studies used classification models for the classification of gait phases and locomotion modes. Meanwhile, 33.3% implemented regression models for the estimation/prediction of kinematic parameters such as joint angles and trajectories, and kinetic parameters such as moments and torques. Deep learning algorithms have been deployed in ~15% of the machine learning implementations. Other methodological parameters were reviewed, such as the sensor selection and the sample sizes used for training the models. |
| first_indexed | 2024-12-16T17:57:26Z |
| format | Article |
| id | doaj.art-98dd4a8afc86475f858ef5d98f7538cc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:57:26Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-98dd4a8afc86475f858ef5d98f7538cc2022-12-21T22:22:08ZengIEEEIEEE Access2169-35362021-01-01911378811381210.1109/ACCESS.2021.31044649513306Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic SystemsRania Kolaghassi0https://orcid.org/0000-0001-8008-6032Mohamad Kenan Al-Hares1https://orcid.org/0000-0003-3246-8479Konstantinos Sirlantzis2https://orcid.org/0000-0002-0847-8880Intelligent Interactions Research Group, University of Kent, Canterbury, U.K.Intelligent Interactions Research Group, University of Kent, Canterbury, U.K.Intelligent Interactions Research Group, University of Kent, Canterbury, U.K.The rate of development of robotic technologies has been meteoric, as a result of compounded advancements in hardware and software. Amongst these robotic technologies are active exoskeletons and orthoses, used in the assistive and rehabilitative fields. Artificial intelligence techniques are increasingly being utilised in gait analysis and prediction. This review paper systematically explores the current use of intelligent algorithms in gait analysis for robotic control, specifically the control of active lower limb exoskeletons and orthoses. Two databases, IEEE and Scopus, were screened for papers published between 1989 to May 2020. 41 papers met the eligibility criteria and were included in this review. 66.7% of the identified studies used classification models for the classification of gait phases and locomotion modes. Meanwhile, 33.3% implemented regression models for the estimation/prediction of kinematic parameters such as joint angles and trajectories, and kinetic parameters such as moments and torques. Deep learning algorithms have been deployed in ~15% of the machine learning implementations. Other methodological parameters were reviewed, such as the sensor selection and the sample sizes used for training the models.https://ieeexplore.ieee.org/document/9513306/Gait analysisexoskeletonsorthosesmachine learningdeep learningwearable robotics |
| spellingShingle | Rania Kolaghassi Mohamad Kenan Al-Hares Konstantinos Sirlantzis Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems IEEE Access Gait analysis exoskeletons orthoses machine learning deep learning wearable robotics |
| title | Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems |
| title_full | Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems |
| title_fullStr | Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems |
| title_full_unstemmed | Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems |
| title_short | Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems |
| title_sort | systematic review of intelligent algorithms in gait analysis and prediction for lower limb robotic systems |
| topic | Gait analysis exoskeletons orthoses machine learning deep learning wearable robotics |
| url | https://ieeexplore.ieee.org/document/9513306/ |
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