Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation
Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stro...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dong-Wook Kim [verfasserIn] Ji Eun Park [verfasserIn] Min-Jung Kim [verfasserIn] Seung Hwan Byun [verfasserIn] Chung In Jung [verfasserIn] Ha Mok Jeong [verfasserIn] Sang Rok Woo [verfasserIn] Kwon Haeng Lee [verfasserIn] Myoung Hwa Lee [verfasserIn] Jung-Woo Jung [verfasserIn] Dayeon Lee [verfasserIn] Byung-Ju Ryu [verfasserIn] Seung Nam Yang [verfasserIn] Seung Jun Baek [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering - IEEE, 2023, 32(2024), Seite 652-661 |
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| Übergeordnetes Werk: |
volume:32 ; year:2024 ; pages:652-661 |
| Links: |
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| DOI / URN: |
10.1109/TNSRE.2024.3358497 |
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| Katalog-ID: |
DOAJ095992073 |
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| 520 | |a Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learning-based assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers. Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model, we developed a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, which can recommend 57 exercises with three levels of difficulty. A prototype of our application was evaluated by potential end-users and achieved a good quality score on the Mobile Application Rating Scale (MARS). | ||
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a hemiplegia | |
| 650 | 4 | |a motion assessment | |
| 650 | 4 | |a self-administered rehabilitation | |
| 650 | 4 | |a upper extremity | |
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| 653 | 0 | |a Therapeutics. Pharmacology | |
| 700 | 0 | |a Ji Eun Park |e verfasserin |4 aut | |
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| 700 | 0 | |a Seung Hwan Byun |e verfasserin |4 aut | |
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| 700 | 0 | |a Seung Nam Yang |e verfasserin |4 aut | |
| 700 | 0 | |a Seung Jun Baek |e verfasserin |4 aut | |
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10.1109/TNSRE.2024.3358497 doi (DE-627)DOAJ095992073 (DE-599)DOAJ562b1b382ad94590b6713b5146a3a886 DE-627 ger DE-627 rakwb eng R855-855.5 RM1-950 Dong-Wook Kim verfasserin aut Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learning-based assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers. Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model, we developed a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, which can recommend 57 exercises with three levels of difficulty. A prototype of our application was evaluated by potential end-users and achieved a good quality score on the Mobile Application Rating Scale (MARS). Deep learning hemiplegia motion assessment self-administered rehabilitation upper extremity Medical technology Therapeutics. Pharmacology Ji Eun Park verfasserin aut Min-Jung Kim verfasserin aut Seung Hwan Byun verfasserin aut Chung In Jung verfasserin aut Ha Mok Jeong verfasserin aut Sang Rok Woo verfasserin aut Kwon Haeng Lee verfasserin aut Myoung Hwa Lee verfasserin aut Jung-Woo Jung verfasserin aut Dayeon Lee verfasserin aut Byung-Ju Ryu verfasserin aut Seung Nam Yang verfasserin aut Seung Jun Baek verfasserin aut In IEEE Transactions on Neural Systems and Rehabilitation Engineering IEEE, 2023 32(2024), Seite 652-661 (DE-627)320614026 (DE-600)2021739-0 15580210 nnns volume:32 year:2024 pages:652-661 https://doi.org/10.1109/TNSRE.2024.3358497 kostenfrei https://doaj.org/article/562b1b382ad94590b6713b5146a3a886 kostenfrei https://ieeexplore.ieee.org/document/10414138/ kostenfrei https://doaj.org/toc/1558-0210 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 32 2024 652-661 |
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10.1109/TNSRE.2024.3358497 doi (DE-627)DOAJ095992073 (DE-599)DOAJ562b1b382ad94590b6713b5146a3a886 DE-627 ger DE-627 rakwb eng R855-855.5 RM1-950 Dong-Wook Kim verfasserin aut Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learning-based assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers. Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model, we developed a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, which can recommend 57 exercises with three levels of difficulty. A prototype of our application was evaluated by potential end-users and achieved a good quality score on the Mobile Application Rating Scale (MARS). Deep learning hemiplegia motion assessment self-administered rehabilitation upper extremity Medical technology Therapeutics. Pharmacology Ji Eun Park verfasserin aut Min-Jung Kim verfasserin aut Seung Hwan Byun verfasserin aut Chung In Jung verfasserin aut Ha Mok Jeong verfasserin aut Sang Rok Woo verfasserin aut Kwon Haeng Lee verfasserin aut Myoung Hwa Lee verfasserin aut Jung-Woo Jung verfasserin aut Dayeon Lee verfasserin aut Byung-Ju Ryu verfasserin aut Seung Nam Yang verfasserin aut Seung Jun Baek verfasserin aut In IEEE Transactions on Neural Systems and Rehabilitation Engineering IEEE, 2023 32(2024), Seite 652-661 (DE-627)320614026 (DE-600)2021739-0 15580210 nnns volume:32 year:2024 pages:652-661 https://doi.org/10.1109/TNSRE.2024.3358497 kostenfrei https://doaj.org/article/562b1b382ad94590b6713b5146a3a886 kostenfrei https://ieeexplore.ieee.org/document/10414138/ kostenfrei https://doaj.org/toc/1558-0210 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 32 2024 652-661 |
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R855-855.5 RM1-950 Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation Deep learning hemiplegia motion assessment self-administered rehabilitation upper extremity |
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Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation |
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Dong-Wook Kim Ji Eun Park Min-Jung Kim Seung Hwan Byun Chung In Jung Ha Mok Jeong Sang Rok Woo Kwon Haeng Lee Myoung Hwa Lee Jung-Woo Jung Dayeon Lee Byung-Ju Ryu Seung Nam Yang Seung Jun Baek |
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automatic assessment of upper extremity function and mobile application for self-administered stroke rehabilitation |
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Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation |
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Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learning-based assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers. Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model, we developed a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, which can recommend 57 exercises with three levels of difficulty. A prototype of our application was evaluated by potential end-users and achieved a good quality score on the Mobile Application Rating Scale (MARS). |
| abstractGer |
Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learning-based assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers. Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model, we developed a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, which can recommend 57 exercises with three levels of difficulty. A prototype of our application was evaluated by potential end-users and achieved a good quality score on the Mobile Application Rating Scale (MARS). |
| abstract_unstemmed |
Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learning-based assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers. Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model, we developed a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, which can recommend 57 exercises with three levels of difficulty. A prototype of our application was evaluated by potential end-users and achieved a good quality score on the Mobile Application Rating Scale (MARS). |
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Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation |
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