Fall Risk Classification Using Trunk Movement Patterns from Inertial Measurement Units and Mini-BESTest in Community-Dwelling Older Adults: A Deep Learning Approach

Vista sencilla de ítem
dc.contributor.authorRobles Cruz, Diego
dc.contributor.authorPuebla Quiñones, Sebastián
dc.contributor.authorLira Belmar, Andrea
dc.contributor.authorQuintana Figueroa, Denisse
dc.contributor.authorReyes Hidalgo, María
dc.contributor.authorTaramasco Toro, Carla
dc.date.accessioned2024-11-11T13:12:32Z
dc.date.available2024-11-11T13:12:32Z
dc.date.issued2024
dc.descriptionIndexación: Scopus.
dc.description.abstractFalls among older adults represent a critical global public health problem, as they are one of the main causes of disability in this age group. We have developed an automated approach to identifying fall risk using low-cost, accessible technology. Trunk movement patterns were collected from 181 older people, with and without a history of falls, during the execution of the Mini-BESTest. Data were captured using smartphone sensors (an accelerometer, a gyroscope, and a magnetometer) and classified based on fall history using deep learning algorithms (LSTM). The classification model achieved an overall accuracy of 88.55% a precision of 90.14%, a recall of 87.93%, and an F1 score of 89.02% by combining all signals from the Mini-BESTest tasks. The performance outperformed the metrics we obtained from individual tasks, demonstrating that aggregating all cues provides a more complete and robust assessment of fall risk in older adults. The results suggest that combining signals from multiple tasks allowed the model to better capture the complexities of postural control and dynamic gait, leading to better prediction of falls. This highlights the potential of integrating multiple assessment modalities for more effective fall risk monitoring.
dc.description.urihttps://www-scopus-com.recursosbiblioteca.unab.cl/record/display.uri?eid=2-s2.0-85207517855&origin=resultslist&sort=plf-f&src=s&sid=f06abd21ed9fd72fccf5b29afb02c858&sot=aff&sdt=cl&cluster=scosubjabbr%2C%22PHYS%22%2Ct%2C%22EART%22%2Ct%2C%22CHEM%22%2Ct%2C%22ENGI%22%2Ct%2C%22MATE%22%2Ct%2Bscolang%2C%22English%22%2Ct%2Bscofreetoread%2C%22all%22%2Ct&s=AF-ID%2860002636%29+AND+SUBJAREA%28PHYS%29&sl=34&sessionSearchId=f06abd21ed9fd72fccf5b29afb02c858&relpos=7
dc.identifier.citationApplied Sciences (Switzerland) Open Access Volume 14, Issue 20 October 2024 Article number 9170
dc.identifier.doi10.3390/app14209170
dc.identifier.issn2076-3417
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/61846
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights.licenseCC BY 4.0 Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDeep learning
dc.subjectFall risk
dc.subjectMini-BESTest
dc.subjectPattern recognition
dc.titleFall Risk Classification Using Trunk Movement Patterns from Inertial Measurement Units and Mini-BESTest in Community-Dwelling Older Adults: A Deep Learning Approach
dc.typeArtículo
Archivos
Bloque original
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
Fall-Risk-Classification-Using-Trunk-Movement-Patterns-from-Inertial-Measurement-Units-and-MiniBESTest-in-CommunityDwelling-Older-Adults-A-Deep-Learning-Approach.pdf
Tamaño:
11.69 MB
Formato:
Adobe Portable Document Format
Descripción:
TEXTO COMPLETO EN INGLÉS
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar
Colecciones
FIng - Artículos de Revista