Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion
<b>Background/Objectives</b>: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality and quality of life. The early detection of these diseases is challenging, often relying on in-person screening, which is difficult to implement regularly. This...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2075-4418/14/24/2886 |
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| author | Rocío Aznar-Gimeno Jose Luis Perez-Lasierra Pablo Pérez-Lázaro Irene Bosque-López Marina Azpíroz-Puente Pilar Salvo-Ibáñez Martin Morita-Hernandez Ana Caren Hernández-Ruiz Antonio Gómez-Bernal María de la Vega Rodrigalvarez-Chamarro José-Víctor Alfaro-Santafé Rafael del Hoyo-Alonso Javier Alfaro-Santafé |
| author_facet | Rocío Aznar-Gimeno Jose Luis Perez-Lasierra Pablo Pérez-Lázaro Irene Bosque-López Marina Azpíroz-Puente Pilar Salvo-Ibáñez Martin Morita-Hernandez Ana Caren Hernández-Ruiz Antonio Gómez-Bernal María de la Vega Rodrigalvarez-Chamarro José-Víctor Alfaro-Santafé Rafael del Hoyo-Alonso Javier Alfaro-Santafé |
| author_sort | Rocío Aznar-Gimeno |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality and quality of life. The early detection of these diseases is challenging, often relying on in-person screening, which is difficult to implement regularly. This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. <b>Methods</b>: A cross-sectional case-control study was conducted involving 42 individuals aged 60 years or older. Participants were classified as having sarcopenia if they met the criteria established by the European Working Group on Sarcopenia in Older People and as having CD if their score in the Mini-Mental State Examination was ≤24 points. Gait patterns were assessed at usual walking speeds using sensors attached to the feet and lumbar region, and CV data were captured using a camera. Several key variables related to gait dynamics were extracted. Finally, machine learning models were developed using these variables to predict sarcopenia and CD. <b>Results</b>: Models based on sensor data, CV data, and a combination of both technologies achieved high predictive accuracy, particularly for CD. The best model for CD achieved an F1-score of 0.914, with a 95% sensitivity and 92% specificity. The combined technologies model for sarcopenia also demonstrated high performance, yielding an F1-score of 0.748 with a 100% sensitivity and 83% specificity. <b>Conclusions</b>: The study demonstrates that gait analysis through sensor and CV fusion can effectively screen for sarcopenia and CD. The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings. |
| format | Article |
| id | doaj-art-63dbce7a0c524cd8b04326a44f37ebc3 |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-63dbce7a0c524cd8b04326a44f37ebc32025-08-20T02:53:38ZengMDPI AGDiagnostics2075-44182024-12-011424288610.3390/diagnostics14242886Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor FusionRocío Aznar-Gimeno0Jose Luis Perez-Lasierra1Pablo Pérez-Lázaro2Irene Bosque-López3Marina Azpíroz-Puente4Pilar Salvo-Ibáñez5Martin Morita-Hernandez6Ana Caren Hernández-Ruiz7Antonio Gómez-Bernal8María de la Vega Rodrigalvarez-Chamarro9José-Víctor Alfaro-Santafé10Rafael del Hoyo-Alonso11Javier Alfaro-Santafé12Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón (ITA), María de Luna 7-8, 50018 Zaragoza, SpainPodoactiva Research & Development Department, Biomechanical Unit, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, SpainDepartment of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón (ITA), María de Luna 7-8, 50018 Zaragoza, SpainDepartment of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón (ITA), María de Luna 7-8, 50018 Zaragoza, SpainPodoactiva Research & Development Department, Biomechanical Unit, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, SpainDepartment of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón (ITA), María de Luna 7-8, 50018 Zaragoza, SpainPodoactiva Research & Development Department, Biomechanical Unit, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, SpainDepartment of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón (ITA), María de Luna 7-8, 50018 Zaragoza, SpainPodoactiva Research & Development Department, Biomechanical Unit, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, SpainDepartment of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón (ITA), María de Luna 7-8, 50018 Zaragoza, SpainPodoactiva Research & Development Department, Biomechanical Unit, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, SpainDepartment of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón (ITA), María de Luna 7-8, 50018 Zaragoza, SpainPodoactiva Research & Development Department, Biomechanical Unit, Parque Tecnológico Walqa, Ctra. N330a Km 566, 22197 Cuarte, Spain<b>Background/Objectives</b>: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality and quality of life. The early detection of these diseases is challenging, often relying on in-person screening, which is difficult to implement regularly. This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. <b>Methods</b>: A cross-sectional case-control study was conducted involving 42 individuals aged 60 years or older. Participants were classified as having sarcopenia if they met the criteria established by the European Working Group on Sarcopenia in Older People and as having CD if their score in the Mini-Mental State Examination was ≤24 points. Gait patterns were assessed at usual walking speeds using sensors attached to the feet and lumbar region, and CV data were captured using a camera. Several key variables related to gait dynamics were extracted. Finally, machine learning models were developed using these variables to predict sarcopenia and CD. <b>Results</b>: Models based on sensor data, CV data, and a combination of both technologies achieved high predictive accuracy, particularly for CD. The best model for CD achieved an F1-score of 0.914, with a 95% sensitivity and 92% specificity. The combined technologies model for sarcopenia also demonstrated high performance, yielding an F1-score of 0.748 with a 100% sensitivity and 83% specificity. <b>Conclusions</b>: The study demonstrates that gait analysis through sensor and CV fusion can effectively screen for sarcopenia and CD. The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings.https://www.mdpi.com/2075-4418/14/24/2886inertial measurement unitwearable sensorartificial intelligencemachine learninghuman pose estimationmusculoskeletal disorders |
| spellingShingle | Rocío Aznar-Gimeno Jose Luis Perez-Lasierra Pablo Pérez-Lázaro Irene Bosque-López Marina Azpíroz-Puente Pilar Salvo-Ibáñez Martin Morita-Hernandez Ana Caren Hernández-Ruiz Antonio Gómez-Bernal María de la Vega Rodrigalvarez-Chamarro José-Víctor Alfaro-Santafé Rafael del Hoyo-Alonso Javier Alfaro-Santafé Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion Diagnostics inertial measurement unit wearable sensor artificial intelligence machine learning human pose estimation musculoskeletal disorders |
| title | Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion |
| title_full | Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion |
| title_fullStr | Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion |
| title_full_unstemmed | Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion |
| title_short | Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion |
| title_sort | gait based ai models for detecting sarcopenia and cognitive decline using sensor fusion |
| topic | inertial measurement unit wearable sensor artificial intelligence machine learning human pose estimation musculoskeletal disorders |
| url | https://www.mdpi.com/2075-4418/14/24/2886 |
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