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Cortical activity and walking speed are known to decline with age and can lead to an increased risk of falls in the elderly. Despite age being a known contributor to this decline, individuals age at different rates. This study aimed to analyse left and right cortical activity changes in elderly adults regarding their walking speed. Cortical activation and gait data were obtained from 50 healthy older individuals. Participants were then grouped into a cluster based on their preferred walking speed (slow or fast). Analyses on the differences of cortical activation and gait parameters between groups were carried out. Within-subject analyses on left and right–hemispheric activation were also performed. Results showed that individuals with a slower preferred walking speed required a higher increase in cortical activity. Individuals in the fast cluster presented greater changes in cortical activation in the right hemisphere. This work demonstrates that categorizing older adults by age is not necessarily the most relevant method, and that cortical activity can be a good indicator of performance with respect to walking speed (linked to fall risk and frailty in the elderly). Future work may wish to explore how physical activity training influences cortical activation over time in the elderly. [ABSTRACT FROM AUTHOR]

Building a prediction model based on both real data and expert knowledge can be challenging. We experienced this difficulty in our context of predicting fall risk factors in the elderly, where two sources of data were available: hand-reported information from medical consultations and expert information from human specialists. As is often the case, the real data were incomplete and imbalanced, and the participation of medical experts required the selection of a kind of model they can understand. This article describes the steps followed in the construction and evaluation of the model, structured as a new framework. The proposed model is a Bayesian network (BN) built from expert knowledge and learning from a real data set, guided by both the understandability of the BN graph and the performance of the model to predict risk factors. The resulting Bayesian network model includes 90 variables to evaluate 10 actionable target risk factors. It demonstrated its potential compared to other classifiers in terms of prediction performance, whilst offering a higher degree of interpretability. • Build a Bayesian Network (BN) by combining expert knowledge and a real data set. • Propose a complete framework to build an efficient and understandable BN model. • Propose a knowledge model including 90 variables and 10 target risk factors for fall. • Predict 10 risk factors for fall and compare with usual classifiers. • Improve the understandability of the BN model thanks to local graphical models. [ABSTRACT FROM AUTHOR]