当前位置: X-MOL 学术IEEE J. Biomed. Health Inform. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Classifying the Risk of Cognitive Impairment Using Sequential Gait Characteristics and Long Short-Term Memory Networks
IEEE Journal of Biomedical and Health Informatics ( IF 7.7 ) Pub Date : 2021-04-15 , DOI: 10.1109/jbhi.2021.3073372
Dawoon Jung , Jinwook Kim , Miji Kim , Chang Won Won , Kyung-Ryoul Mun

Cognitive impairment in the elderly causes a significant decline in the quality of life and a substantial economic burden on society. Yet, diagnosing cognitive impairment is apt to be missed or delayed due to its assessment being laborious. This study aimed to propose a new approach of classifying the risk of cognitive impairment in the elderly using sequential gait characteristics and machine learning techniques. A total of 108 community-dwelling elderly individuals participated in this study. The participants were categorized into three groups based on their scores of the mini-mental state examination. Each participant completed both the usual- and fast-paced walking on the straight path with two gyroscopes on each foot. By analyzing the foot sagittal angular velocity signals, the temporal gait parameters were extracted from each gait cycle. Five classical machine learning models and a long short-term memory network were respectively employed to produce the classifiers that used the time-consecutive temporal gait parameters as predictors of cognitive impairment. Five-fold cross-validation was applied to 70% of the data in each group, and the remaining 30% were used as test data. An F 1 -score of 0.974 was achieved in classifying the three groups by the long short-term memory network-based classifier that used the double-limb support, stance, step, and stride times at usual-paced walking and the double- and single-limb support, stance, and stride times at fast-paced walking as inputs. The proposed approach would pave the way for earlier diagnosis of cognitive impairment in non-clinical settings without professional help, which can facilitate more timely intervention.

中文翻译:

使用顺序步态特征和长短期记忆网络对认知障碍的风险进行分类

老年人的认知障碍导致生活质量显着下降,给社会带来沉重的经济负担。然而,由于其评估费力,因此容易错过或延迟诊断认知障碍。本研究旨在提出一种使用顺序步态特征和机器学习技术对老年人认知障碍风险进行分类的新方法。共有 108 名社区居住的老年人参加了这项研究。参与者根据他们的小型精神状态检查的分数被分为三组。每位参与者都在每只脚上有两个陀螺仪的笔直路径上完成了通常和快节奏的行走。通过分析足部矢状角速度信号,从每个步态周期中提取时间步态参数。分别采用五个经典机器学习模型和一个长短期记忆网络来生成使用时间连续时间步态参数作为认知障碍预测因子的分类器。每组70%的数据采用五折交叉验证,其余30%作为测试数据。安芳 1 - 通过基于长短期记忆网络的分类器对三组进行分类,得分为 0.974,该分类器使用双肢支撑、站立、步幅和步幅时间在通常步调步行和双人和单人快节奏步行时的肢体支撑、站姿和步幅时间作为输入。拟议的方法将为在没有专业帮助的情况下在非临床环境中早期诊断认知障碍铺平道路,这可以促进更及时的干预。
更新日期:2021-04-15
down
wechat
bug