Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease
Abstract One of the disorders that affects the central nervous system the most severely is Parkinson's disease (PD). In 2019, the World Health Organization (WHO) reported that PD claimed the lives of 0.33 million people, an increase of almost 100% since 2000. The disease also caused 5.8 million...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gunaratne, D. A. N. P. [verfasserIn] Herath, H. M. K. K. M. B. [verfasserIn] Dhanushi, R. G. D. [verfasserIn] Yasakethu, S. L. P. [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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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: SN Computer Science - Springer Nature Singapore, 2020, 5(2024), 7 vom: 07. Okt. |
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| Übergeordnetes Werk: |
volume:5 ; year:2024 ; number:7 ; day:07 ; month:10 |
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| DOI / URN: |
10.1007/s42979-024-03295-1 |
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SPR057705798 |
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| 520 | |a Abstract One of the disorders that affects the central nervous system the most severely is Parkinson's disease (PD). In 2019, the World Health Organization (WHO) reported that PD claimed the lives of 0.33 million people, an increase of almost 100% since 2000. The disease also caused 5.8 million disability-adjusted life years, an 81% increase since 2000. This emphasizes how dangerous PD may be in home settings, especially for the elderly. Currently, clinical approaches continue to be the mainstay of PD screening. Still, there's hope, thanks to developments in wearable sensor-based identification techniques. Nevertheless, methods such as the vision attentive paradigm are required to guarantee usability because older adults find them uncomfortable. Current systems frequently depend on isolated evaluations, which the WHO considers inadequate for thoroughly assessing PD through functional mobilities. This research aims to evaluate older persons with PD to close this gap. Timed Up and Go (TUG) time, gait speed, and fall score are the three main components integrated with the proposed system. The TUG test, gait speed, and fall ratio were validated using the vision attentive model and the traditional clinical method. Ethical norms were followed when testing in homes, hospitals, and elder care institutions. The suggested method's results show great potential, with an impressive 90.02% (precision 0.89) accuracy rate in identifying PD patients. | ||
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10.1007/s42979-024-03295-1 doi (DE-627)SPR057705798 (SPR)s42979-024-03295-1-e DE-627 ger DE-627 rakwb eng Gunaratne, D. A. N. P. verfasserin aut Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract One of the disorders that affects the central nervous system the most severely is Parkinson's disease (PD). In 2019, the World Health Organization (WHO) reported that PD claimed the lives of 0.33 million people, an increase of almost 100% since 2000. The disease also caused 5.8 million disability-adjusted life years, an 81% increase since 2000. This emphasizes how dangerous PD may be in home settings, especially for the elderly. Currently, clinical approaches continue to be the mainstay of PD screening. Still, there's hope, thanks to developments in wearable sensor-based identification techniques. Nevertheless, methods such as the vision attentive paradigm are required to guarantee usability because older adults find them uncomfortable. Current systems frequently depend on isolated evaluations, which the WHO considers inadequate for thoroughly assessing PD through functional mobilities. This research aims to evaluate older persons with PD to close this gap. Timed Up and Go (TUG) time, gait speed, and fall score are the three main components integrated with the proposed system. The TUG test, gait speed, and fall ratio were validated using the vision attentive model and the traditional clinical method. Ethical norms were followed when testing in homes, hospitals, and elder care institutions. The suggested method's results show great potential, with an impressive 90.02% (precision 0.89) accuracy rate in identifying PD patients. Artificial Intelligence (AI) (dpeaa)DE-He213 Elderly Healthcare (dpeaa)DE-He213 Functional Mobilities (dpeaa)DE-He213 Parkinson's Disease (PD) (dpeaa)DE-He213 Vision Attentive Systems (dpeaa)DE-He213 Herath, H. M. K. K. M. B. verfasserin (orcid)0000-0002-1873-768X aut Dhanushi, R. G. D. verfasserin aut Yasakethu, S. L. P. verfasserin aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 7 vom: 07. Okt. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:7 day:07 month:10 https://dx.doi.org/10.1007/s42979-024-03295-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 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_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2574 GBV_ILN_4035 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 7 07 10 |
| spelling |
10.1007/s42979-024-03295-1 doi (DE-627)SPR057705798 (SPR)s42979-024-03295-1-e DE-627 ger DE-627 rakwb eng Gunaratne, D. A. N. P. verfasserin aut Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract One of the disorders that affects the central nervous system the most severely is Parkinson's disease (PD). In 2019, the World Health Organization (WHO) reported that PD claimed the lives of 0.33 million people, an increase of almost 100% since 2000. The disease also caused 5.8 million disability-adjusted life years, an 81% increase since 2000. This emphasizes how dangerous PD may be in home settings, especially for the elderly. Currently, clinical approaches continue to be the mainstay of PD screening. Still, there's hope, thanks to developments in wearable sensor-based identification techniques. Nevertheless, methods such as the vision attentive paradigm are required to guarantee usability because older adults find them uncomfortable. Current systems frequently depend on isolated evaluations, which the WHO considers inadequate for thoroughly assessing PD through functional mobilities. This research aims to evaluate older persons with PD to close this gap. Timed Up and Go (TUG) time, gait speed, and fall score are the three main components integrated with the proposed system. The TUG test, gait speed, and fall ratio were validated using the vision attentive model and the traditional clinical method. Ethical norms were followed when testing in homes, hospitals, and elder care institutions. The suggested method's results show great potential, with an impressive 90.02% (precision 0.89) accuracy rate in identifying PD patients. Artificial Intelligence (AI) (dpeaa)DE-He213 Elderly Healthcare (dpeaa)DE-He213 Functional Mobilities (dpeaa)DE-He213 Parkinson's Disease (PD) (dpeaa)DE-He213 Vision Attentive Systems (dpeaa)DE-He213 Herath, H. M. K. K. M. B. verfasserin (orcid)0000-0002-1873-768X aut Dhanushi, R. G. D. verfasserin aut Yasakethu, S. L. P. verfasserin aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 7 vom: 07. Okt. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:7 day:07 month:10 https://dx.doi.org/10.1007/s42979-024-03295-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 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_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2574 GBV_ILN_4035 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 7 07 10 |
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10.1007/s42979-024-03295-1 doi (DE-627)SPR057705798 (SPR)s42979-024-03295-1-e DE-627 ger DE-627 rakwb eng Gunaratne, D. A. N. P. verfasserin aut Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract One of the disorders that affects the central nervous system the most severely is Parkinson's disease (PD). In 2019, the World Health Organization (WHO) reported that PD claimed the lives of 0.33 million people, an increase of almost 100% since 2000. The disease also caused 5.8 million disability-adjusted life years, an 81% increase since 2000. This emphasizes how dangerous PD may be in home settings, especially for the elderly. Currently, clinical approaches continue to be the mainstay of PD screening. Still, there's hope, thanks to developments in wearable sensor-based identification techniques. Nevertheless, methods such as the vision attentive paradigm are required to guarantee usability because older adults find them uncomfortable. Current systems frequently depend on isolated evaluations, which the WHO considers inadequate for thoroughly assessing PD through functional mobilities. This research aims to evaluate older persons with PD to close this gap. Timed Up and Go (TUG) time, gait speed, and fall score are the three main components integrated with the proposed system. The TUG test, gait speed, and fall ratio were validated using the vision attentive model and the traditional clinical method. Ethical norms were followed when testing in homes, hospitals, and elder care institutions. The suggested method's results show great potential, with an impressive 90.02% (precision 0.89) accuracy rate in identifying PD patients. Artificial Intelligence (AI) (dpeaa)DE-He213 Elderly Healthcare (dpeaa)DE-He213 Functional Mobilities (dpeaa)DE-He213 Parkinson's Disease (PD) (dpeaa)DE-He213 Vision Attentive Systems (dpeaa)DE-He213 Herath, H. M. K. K. M. B. verfasserin (orcid)0000-0002-1873-768X aut Dhanushi, R. G. D. verfasserin aut Yasakethu, S. L. P. verfasserin aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 7 vom: 07. Okt. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:7 day:07 month:10 https://dx.doi.org/10.1007/s42979-024-03295-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 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_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2574 GBV_ILN_4035 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 7 07 10 |
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10.1007/s42979-024-03295-1 doi (DE-627)SPR057705798 (SPR)s42979-024-03295-1-e DE-627 ger DE-627 rakwb eng Gunaratne, D. A. N. P. verfasserin aut Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract One of the disorders that affects the central nervous system the most severely is Parkinson's disease (PD). In 2019, the World Health Organization (WHO) reported that PD claimed the lives of 0.33 million people, an increase of almost 100% since 2000. The disease also caused 5.8 million disability-adjusted life years, an 81% increase since 2000. This emphasizes how dangerous PD may be in home settings, especially for the elderly. Currently, clinical approaches continue to be the mainstay of PD screening. Still, there's hope, thanks to developments in wearable sensor-based identification techniques. Nevertheless, methods such as the vision attentive paradigm are required to guarantee usability because older adults find them uncomfortable. Current systems frequently depend on isolated evaluations, which the WHO considers inadequate for thoroughly assessing PD through functional mobilities. This research aims to evaluate older persons with PD to close this gap. Timed Up and Go (TUG) time, gait speed, and fall score are the three main components integrated with the proposed system. The TUG test, gait speed, and fall ratio were validated using the vision attentive model and the traditional clinical method. Ethical norms were followed when testing in homes, hospitals, and elder care institutions. The suggested method's results show great potential, with an impressive 90.02% (precision 0.89) accuracy rate in identifying PD patients. Artificial Intelligence (AI) (dpeaa)DE-He213 Elderly Healthcare (dpeaa)DE-He213 Functional Mobilities (dpeaa)DE-He213 Parkinson's Disease (PD) (dpeaa)DE-He213 Vision Attentive Systems (dpeaa)DE-He213 Herath, H. M. K. K. M. B. verfasserin (orcid)0000-0002-1873-768X aut Dhanushi, R. G. D. verfasserin aut Yasakethu, S. L. P. verfasserin aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 7 vom: 07. Okt. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:7 day:07 month:10 https://dx.doi.org/10.1007/s42979-024-03295-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 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_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2574 GBV_ILN_4035 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 7 07 10 |
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10.1007/s42979-024-03295-1 doi (DE-627)SPR057705798 (SPR)s42979-024-03295-1-e DE-627 ger DE-627 rakwb eng Gunaratne, D. A. N. P. verfasserin aut Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract One of the disorders that affects the central nervous system the most severely is Parkinson's disease (PD). In 2019, the World Health Organization (WHO) reported that PD claimed the lives of 0.33 million people, an increase of almost 100% since 2000. The disease also caused 5.8 million disability-adjusted life years, an 81% increase since 2000. This emphasizes how dangerous PD may be in home settings, especially for the elderly. Currently, clinical approaches continue to be the mainstay of PD screening. Still, there's hope, thanks to developments in wearable sensor-based identification techniques. Nevertheless, methods such as the vision attentive paradigm are required to guarantee usability because older adults find them uncomfortable. Current systems frequently depend on isolated evaluations, which the WHO considers inadequate for thoroughly assessing PD through functional mobilities. This research aims to evaluate older persons with PD to close this gap. Timed Up and Go (TUG) time, gait speed, and fall score are the three main components integrated with the proposed system. The TUG test, gait speed, and fall ratio were validated using the vision attentive model and the traditional clinical method. Ethical norms were followed when testing in homes, hospitals, and elder care institutions. The suggested method's results show great potential, with an impressive 90.02% (precision 0.89) accuracy rate in identifying PD patients. Artificial Intelligence (AI) (dpeaa)DE-He213 Elderly Healthcare (dpeaa)DE-He213 Functional Mobilities (dpeaa)DE-He213 Parkinson's Disease (PD) (dpeaa)DE-He213 Vision Attentive Systems (dpeaa)DE-He213 Herath, H. M. K. K. M. B. verfasserin (orcid)0000-0002-1873-768X aut Dhanushi, R. G. D. verfasserin aut Yasakethu, S. L. P. verfasserin aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 7 vom: 07. Okt. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:7 day:07 month:10 https://dx.doi.org/10.1007/s42979-024-03295-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 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_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2574 GBV_ILN_4035 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 7 07 10 |
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P.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. 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Gunaratne, D. A. N. P. |
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Gunaratne, D. A. N. P. misc Artificial Intelligence (AI) misc Elderly Healthcare misc Functional Mobilities misc Parkinson's Disease (PD) misc Vision Attentive Systems Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease |
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Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease Artificial Intelligence (AI) (dpeaa)DE-He213 Elderly Healthcare (dpeaa)DE-He213 Functional Mobilities (dpeaa)DE-He213 Parkinson's Disease (PD) (dpeaa)DE-He213 Vision Attentive Systems (dpeaa)DE-He213 |
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evaluation of functional mobility of elders using vision attentive model for parkinson’s disease |
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Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease |
| abstract |
Abstract One of the disorders that affects the central nervous system the most severely is Parkinson's disease (PD). In 2019, the World Health Organization (WHO) reported that PD claimed the lives of 0.33 million people, an increase of almost 100% since 2000. The disease also caused 5.8 million disability-adjusted life years, an 81% increase since 2000. This emphasizes how dangerous PD may be in home settings, especially for the elderly. Currently, clinical approaches continue to be the mainstay of PD screening. Still, there's hope, thanks to developments in wearable sensor-based identification techniques. Nevertheless, methods such as the vision attentive paradigm are required to guarantee usability because older adults find them uncomfortable. Current systems frequently depend on isolated evaluations, which the WHO considers inadequate for thoroughly assessing PD through functional mobilities. This research aims to evaluate older persons with PD to close this gap. Timed Up and Go (TUG) time, gait speed, and fall score are the three main components integrated with the proposed system. The TUG test, gait speed, and fall ratio were validated using the vision attentive model and the traditional clinical method. Ethical norms were followed when testing in homes, hospitals, and elder care institutions. The suggested method's results show great potential, with an impressive 90.02% (precision 0.89) accuracy rate in identifying PD patients. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract One of the disorders that affects the central nervous system the most severely is Parkinson's disease (PD). In 2019, the World Health Organization (WHO) reported that PD claimed the lives of 0.33 million people, an increase of almost 100% since 2000. The disease also caused 5.8 million disability-adjusted life years, an 81% increase since 2000. This emphasizes how dangerous PD may be in home settings, especially for the elderly. Currently, clinical approaches continue to be the mainstay of PD screening. Still, there's hope, thanks to developments in wearable sensor-based identification techniques. Nevertheless, methods such as the vision attentive paradigm are required to guarantee usability because older adults find them uncomfortable. Current systems frequently depend on isolated evaluations, which the WHO considers inadequate for thoroughly assessing PD through functional mobilities. This research aims to evaluate older persons with PD to close this gap. Timed Up and Go (TUG) time, gait speed, and fall score are the three main components integrated with the proposed system. The TUG test, gait speed, and fall ratio were validated using the vision attentive model and the traditional clinical method. Ethical norms were followed when testing in homes, hospitals, and elder care institutions. The suggested method's results show great potential, with an impressive 90.02% (precision 0.89) accuracy rate in identifying PD patients. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract One of the disorders that affects the central nervous system the most severely is Parkinson's disease (PD). In 2019, the World Health Organization (WHO) reported that PD claimed the lives of 0.33 million people, an increase of almost 100% since 2000. The disease also caused 5.8 million disability-adjusted life years, an 81% increase since 2000. This emphasizes how dangerous PD may be in home settings, especially for the elderly. Currently, clinical approaches continue to be the mainstay of PD screening. Still, there's hope, thanks to developments in wearable sensor-based identification techniques. Nevertheless, methods such as the vision attentive paradigm are required to guarantee usability because older adults find them uncomfortable. Current systems frequently depend on isolated evaluations, which the WHO considers inadequate for thoroughly assessing PD through functional mobilities. This research aims to evaluate older persons with PD to close this gap. Timed Up and Go (TUG) time, gait speed, and fall score are the three main components integrated with the proposed system. The TUG test, gait speed, and fall ratio were validated using the vision attentive model and the traditional clinical method. Ethical norms were followed when testing in homes, hospitals, and elder care institutions. The suggested method's results show great potential, with an impressive 90.02% (precision 0.89) accuracy rate in identifying PD patients. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Evaluation of Functional Mobility of Elders Using Vision Attentive Model for Parkinson’s Disease |
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Herath, H. M. K. K. M. B. Dhanushi, R. G. D. Yasakethu, S. L. P. |
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| score |
7.399781 |