Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM
Abstract Gait and motion stability analysis in gait dysfunction problems is a very interesting research area. Usually, patients who undergo vestibular deafferentation are affected by changes in their dynamic balance. Therefore, it is important both patients and physicians are able to monitor the pro...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-06-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00280-z |
| _version_ | 1827928490583785472 |
|---|---|
| author | Mohsen Shayestegan Jan Kohout Kateřina Trnková Martin Chovanec Jan Mareš |
| author_facet | Mohsen Shayestegan Jan Kohout Kateřina Trnková Martin Chovanec Jan Mareš |
| author_sort | Mohsen Shayestegan |
| collection | DOAJ |
| description | Abstract Gait and motion stability analysis in gait dysfunction problems is a very interesting research area. Usually, patients who undergo vestibular deafferentation are affected by changes in their dynamic balance. Therefore, it is important both patients and physicians are able to monitor the progress of the so-called vestibular compensation to observe the rehabilitation process objectively. Currently, the quantification of their progress is highly dependent on the physician’s opinion. In this article, we designed a novel methodology to classify the gait disorders associated with unilateral vestibular deafferentation in patients undergoing vestibular schwannoma surgery (model of complete vestibular loss associated with imbalance due to vestibular nerve section and eventual labyrinthectomy). We present a dual-head attentional transformer-LSTM (DHAT-LSTM) to evaluate the problem of rehabilitation from gait dysfunction, which is observed by a Kinect. A system consisting of a key-point-RCNN detector is used to compute body landmark measures and evaluate gait dysfunction based on a DHAT-LSTM network. This structure is used to quantitatively assess gait classification by tracking skeletal features based on the temporal variation of feature sequences. The proposed deep network analyses the features of the patient’s movement. These extracted high-level representations are then fed to the final evaluation of gait dysfunction. The result analytically demonstrates its effectiveness in classification evaluation when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. An accuracy greater than 81% was achieved for given sets of individuals using velocity-based, angle-based, and position features for both the whole body and the symmetric features of the body. |
| first_indexed | 2024-03-13T06:08:24Z |
| format | Article |
| id | doaj.art-67bbd5676b8a4208986c622bb9f07dc5 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-13T06:08:24Z |
| publishDate | 2023-06-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-67bbd5676b8a4208986c622bb9f07dc52023-06-11T11:24:55ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-06-0116111810.1007/s44196-023-00280-zMotion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTMMohsen Shayestegan0Jan Kohout1Kateřina Trnková2Martin Chovanec3Jan Mareš4Faculty of Electrical Engineering and Informatics, University of PardubiceDepartment of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology PragueDepartment of Otorhinolaryngology, Charles University Prague, 3rd Faculty of Medicine, University Hospital Kralovske VinohradyDepartment of Otorhinolaryngology, Charles University Prague, 3rd Faculty of Medicine, University Hospital Kralovske VinohradyFaculty of Electrical Engineering and Informatics, University of PardubiceAbstract Gait and motion stability analysis in gait dysfunction problems is a very interesting research area. Usually, patients who undergo vestibular deafferentation are affected by changes in their dynamic balance. Therefore, it is important both patients and physicians are able to monitor the progress of the so-called vestibular compensation to observe the rehabilitation process objectively. Currently, the quantification of their progress is highly dependent on the physician’s opinion. In this article, we designed a novel methodology to classify the gait disorders associated with unilateral vestibular deafferentation in patients undergoing vestibular schwannoma surgery (model of complete vestibular loss associated with imbalance due to vestibular nerve section and eventual labyrinthectomy). We present a dual-head attentional transformer-LSTM (DHAT-LSTM) to evaluate the problem of rehabilitation from gait dysfunction, which is observed by a Kinect. A system consisting of a key-point-RCNN detector is used to compute body landmark measures and evaluate gait dysfunction based on a DHAT-LSTM network. This structure is used to quantitatively assess gait classification by tracking skeletal features based on the temporal variation of feature sequences. The proposed deep network analyses the features of the patient’s movement. These extracted high-level representations are then fed to the final evaluation of gait dysfunction. The result analytically demonstrates its effectiveness in classification evaluation when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. An accuracy greater than 81% was achieved for given sets of individuals using velocity-based, angle-based, and position features for both the whole body and the symmetric features of the body.https://doi.org/10.1007/s44196-023-00280-zDeep learningClassificationGait disordersVision transformerLSTMTPCNN |
| spellingShingle | Mohsen Shayestegan Jan Kohout Kateřina Trnková Martin Chovanec Jan Mareš Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM International Journal of Computational Intelligence Systems Deep learning Classification Gait disorders Vision transformer LSTM TPCNN |
| title | Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM |
| title_full | Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM |
| title_fullStr | Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM |
| title_full_unstemmed | Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM |
| title_short | Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM |
| title_sort | motion tracking in diagnosis gait disorders classification with a dual head attentional transformer lstm |
| topic | Deep learning Classification Gait disorders Vision transformer LSTM TPCNN |
| url | https://doi.org/10.1007/s44196-023-00280-z |
| work_keys_str_mv | AT mohsenshayestegan motiontrackingindiagnosisgaitdisordersclassificationwithadualheadattentionaltransformerlstm AT jankohout motiontrackingindiagnosisgaitdisordersclassificationwithadualheadattentionaltransformerlstm AT katerinatrnkova motiontrackingindiagnosisgaitdisordersclassificationwithadualheadattentionaltransformerlstm AT martinchovanec motiontrackingindiagnosisgaitdisordersclassificationwithadualheadattentionaltransformerlstm AT janmares motiontrackingindiagnosisgaitdisordersclassificationwithadualheadattentionaltransformerlstm |