Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults.

Autor: Robles Cruz D; Escuela de Ingeniería Civil Informática, Universidad de Valparaíso, Valparaíso 2361827, Chile.; Centro de Estudios del Movimiento Humano, Escuela de Kinesiología, Facultad de Salud y Odontología, Universidad Diego Portales, Santiago 8370076, Chile.; Instituto de Tecnología para la Innovación en Salud y Bienestar, Facultad de Ingeniería, Universidad Andrés Bello, Viña del Mar 2520000, Chile., Lira Belmar A; Center of Interdisciplinary Biomedical and Engineering Research for Health-MEDING Universidad de Valparaíso, Valparaíso 2520000, Chile., Fleury A; IMT Nord Europe, Institut Mines Télécom, Centre for Digital Systems, 59650 Villeneuve d'Ascq, France., Lam M; IMT Nord Europe, Institut Mines Télécom, Centre for Digital Systems, 59650 Villeneuve d'Ascq, France., Castro Andrade RM; Group 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, Brazil., Puebla Quiñones S; Instituto de Tecnología para la Innovación en Salud y Bienestar, Facultad de Ingeniería, Universidad Andrés Bello, Viña del Mar 2520000, Chile., Taramasco Toro C; Instituto de Tecnología para la Innovación en Salud y Bienestar, Facultad de Ingeniería, Universidad Andrés Bello, Viña del Mar 2520000, Chile.; Millennium Nucleus on Sociomedicine, Temuco 4811230, Chile.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Nov 29; Vol. 24 (23). Date of Electronic Publication: 2024 Nov 29.
DOI: 10.3390/s24237651
Abstrakt: 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.
Databáze: MEDLINE
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