Towards environment-aware fall risk assessment: Classifying walking surface conditions using IMU-Based Gait Data and deep learning
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Multidisciplinary Digital Publishing Institute (MDPI)
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Fall risk assessment (FRA) helps clinicians make decisions about the best preventative measures to lower the risk of falls by identifying the different risks that are specific to an individual. With the development of wearable technologies such as inertial measurement units (IMUs), several free-living FRA methods based on fall predictors derived from IMU-based data have been introduced. The performance of such methods could be improved by increasing awareness of the individuals’ walking environment. This study aims to introduce and analyze a 25-layer convolutional neural network model for classifying nine walking surface conditions using IMU-based gait data, providing a basis for environment-aware FRAs. A database containing data collected from thirty participants who wore six IMU sensors while walking on nine surface conditions was employed. A systematic analysis was conducted to determine the effects of gait signals (acceleration, magnetic field, and rate of turn), sensor placement, and signal segment size on the method’s performance. Accuracies of 0.935 and 0.969 were achieved using a single and dual sensor, respectively, reaching an accuracy of 0.971 in the best-case scenario with optimal settings. The findings and analysis can help to develop more reliable and interpretable fall predictors, eventually leading to environment-aware FRA methods.
Açıklama
Anahtar Kelimeler
Convolutional neural networks, Fall risk analysis, Inertial measurement units, Irregular walking surfaces, Walking surface detection
Kaynak
Brain Sciences
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
13
Sayı
10
Künye
Yıldız, A. (2023). Towards environment-aware fall risk assessment: Classifying walking surface conditions using IMU-Based Gait Data and deep learning. Brain Sciences, 13(10), 1-19.
