Jung, D. Kim, J. Kim, M. Won, C.W. Mun, K. 2024-01-19T14:01:55Z 2024-01-19T14:01:55Z 2021-09-02 2021-09 2168-2194 https://pubs.kist.re.kr/handle/201004/116563 Faced with the rapidly aging world population, frailty has emerged as a major health burden among the elderly. This study aimed to investigate the feasibility of using temporal gait characteristics and a long short-term memory network for assessing frailty. Seventy-four community-dwelling elderly individuals participated in this study. The participants were categorized into three groups by their FRAIL scale: robust, pre-frail, and frail groups. The participants completed a 7-meter walking at the self-selected pace with a gyroscope on each foot. Analyzing the gyroscopic data produced seven temporal gait parameters per each gait cycle. Enumerating six consecutive values of each gait parameter produced the gait sequence features which were used as frailty predictors along with the demographic features. Five-fold cross-validation was applied to 70% of the data, and the remaining 30% were used as test data. An F1-score of 0.931 was achieved in classifying the robust, pre-frail, and frail groups by the random forest model trained with age, sex, and the outputs of the long short-term memory network-based classifier that used the initial and terminal double-limb support, step, and stride times as inputs. The proposed approach of assessing frailty using the arrhythmic gait pattern of the elderly and machine learning technique is novel and promising. Pioneering a way that self-monitor frailty at home without any help from experts, the study can contribute to early diagnosis of frailty and make timely medical intervention possible. IEEE English Institute of Electrical and Electronics Engineers Inc. Brain Decision trees Diagnosis Learning systems Long short-term memory Population dynamics Cross validation Demographic features Gait parameters Machine learning techniques Medical intervention Random forest modeling Short term memory World population Gait analysis Frailty Assessment Using Temporal Gait Characteristics and a Long Short-Term Memory Network Article 10.1109/JBHI.2021.3067931 1 IEEE Journal of Biomedical and Health Informatics, v.25, no.9, pp.3649 - 3658 IEEE Journal of Biomedical and Health Informatics 25 9 3649 3658 scie scopus 000692596400043 2-s2.0-85103303599 Computer Science, Information Systems Computer Science, Interdisciplinary Applications Mathematical & Computational Biology Medical Informatics Computer Science Mathematical & Computational Biology Medical Informatics Article Brain Decision trees Diagnosis Learning systems Long short-term memory Population dynamics Cross validation Demographic features Gait parameters Machine learning techniques Medical intervention Random forest modeling Short term memory World population Gait analysis Aging Angular velocity Foot Frailty gait Legged locomotion long short-term memory network machine learning pre-frailty Senior citizens Sociology Statistics