Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults
Community mobility, encompassing both active (e.g., walking) and passive (e.g., driving) transport, plays a crucial role in maintaining autonomy and social interaction among older adults. This study aimed to quantify community mobility in older adults and explore the relationship between GPS- and ac...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7651 |
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| author | Diego Robles Cruz Andrea Lira Belmar Anthony Fleury Méline Lam Rossana M. Castro Andrade Sebastián Puebla Quiñones Carla Taramasco Toro |
| author_facet | Diego Robles Cruz Andrea Lira Belmar Anthony Fleury Méline Lam Rossana M. Castro Andrade Sebastián Puebla Quiñones Carla Taramasco Toro |
| author_sort | Diego Robles Cruz |
| collection | DOAJ |
| description | Community mobility, encompassing both active (e.g., walking) and passive (e.g., driving) transport, plays a crucial role in maintaining autonomy and social interaction among older adults. This study aimed to quantify community mobility in older adults and explore the relationship between GPS- and accelerometer-derived metrics and fall risk. Methods: A total of 129 older adults, with and without a history of falls, were monitored over an 8 h period using GPS and accelerometer data. Three experimental conditions were evaluated: GPS data alone, accelerometer data alone, and a combination of both. Classification models, including Random Forest (RF), Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN), were employed to classify participants based on their fall history. Results: For GPS data alone, RF achieved 74% accuracy, while SVM and KNN reached 67% and 62%, respectively. Using accelerometer data, RF achieved 95% accuracy, and both SVM and KNN achieved 90%. Combining GPS and accelerometer data improved model performance, with RF reaching 97% accuracy, SVM achieving 95%, and KNN 87%. Conclusion: The integration of GPS and accelerometer data significantly enhances the accuracy of distinguishing older adults with and without a history of falls. These findings highlight the potential of sensor-based approaches for accurate fall risk assessment in community-dwelling older adults. |
| format | Article |
| id | doaj-art-6b83a045a1594017abefc197cee7beb4 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj-art-6b83a045a1594017abefc197cee7beb42025-08-20T02:50:37ZengMDPI AGSensors1424-82202024-11-012423765110.3390/s24237651Relationship of Community Mobility, Vital Space, and Faller Status in Older AdultsDiego Robles Cruz0Andrea Lira Belmar1Anthony Fleury2Méline Lam3Rossana M. Castro Andrade4Sebastián Puebla Quiñones5Carla Taramasco Toro6Escuela de Ingeniería Civil Informática, Universidad de Valparaíso, Valparaíso 2361827, ChileCenter of Interdisciplinary Biomedical and Engineering Research for Health—MEDING Universidad de Valparaíso, Valparaíso 2520000, ChileIMT Nord Europe, Institut Mines Télécom, Centre for Digital Systems, 59650 Villeneuve d’Ascq, FranceIMT Nord Europe, Institut Mines Télécom, Centre for Digital Systems, 59650 Villeneuve d’Ascq, FranceGroup of Computer Networks, Software Engineering and Systems (GREat), Computer Science Department (DC), Federal University of Ceará (UFC), Campus do Pici, Bloco 910, Fortaleza 60440-900, BrazilInstituto de Tecnología para la Innovación en Salud y Bienestar, Facultad de Ingeniería, Universidad Andrés Bello, Viña del Mar 2520000, ChileInstituto de Tecnología para la Innovación en Salud y Bienestar, Facultad de Ingeniería, Universidad Andrés Bello, Viña del Mar 2520000, ChileCommunity mobility, encompassing both active (e.g., walking) and passive (e.g., driving) transport, plays a crucial role in maintaining autonomy and social interaction among older adults. This study aimed to quantify community mobility in older adults and explore the relationship between GPS- and accelerometer-derived metrics and fall risk. Methods: A total of 129 older adults, with and without a history of falls, were monitored over an 8 h period using GPS and accelerometer data. Three experimental conditions were evaluated: GPS data alone, accelerometer data alone, and a combination of both. Classification models, including Random Forest (RF), Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN), were employed to classify participants based on their fall history. Results: For GPS data alone, RF achieved 74% accuracy, while SVM and KNN reached 67% and 62%, respectively. Using accelerometer data, RF achieved 95% accuracy, and both SVM and KNN achieved 90%. Combining GPS and accelerometer data improved model performance, with RF reaching 97% accuracy, SVM achieving 95%, and KNN 87%. Conclusion: The integration of GPS and accelerometer data significantly enhances the accuracy of distinguishing older adults with and without a history of falls. These findings highlight the potential of sensor-based approaches for accurate fall risk assessment in community-dwelling older adults.https://www.mdpi.com/1424-8220/24/23/7651fall riskcommunity mobilitygait patterns |
| spellingShingle | Diego Robles Cruz Andrea Lira Belmar Anthony Fleury Méline Lam Rossana M. Castro Andrade Sebastián Puebla Quiñones Carla Taramasco Toro Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults Sensors fall risk community mobility gait patterns |
| title | Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults |
| title_full | Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults |
| title_fullStr | Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults |
| title_full_unstemmed | Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults |
| title_short | Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults |
| title_sort | relationship of community mobility vital space and faller status in older adults |
| topic | fall risk community mobility gait patterns |
| url | https://www.mdpi.com/1424-8220/24/23/7651 |
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