可穿戴计算机
计算机科学
比例(比率)
风险评估
步态
鉴定(生物学)
人工智能
可穿戴技术
防坠落
毒物控制
机器学习
物理医学与康复
医学
伤害预防
计算机安全
医疗急救
嵌入式系统
物理
植物
量子力学
生物
作者
Shibin Wu,Jianlin Ou,Lin Shu,Guohua Hu,Zhen Song,Xiangmin Xu,Zhuoming Chen
标识
DOI:10.1016/j.compbiomed.2022.105355
摘要
Continuous fall risk assessment and real-time high falling risk warning are extremely necessary for the elderly, to protect their lives and ensure their quality of life. Wearable in-shoe pressure sensors have the potential to achieve these targets, due to their adequate wearing comfort. However, it is a great challenge to remove the individual differences of foot pressure data and identify the accurate fall risk from fewer gait cycles to realize real-time warning. We explored a hierarchical deep learning network named MhNet for real-time fall risk assessment, which utilized the advantages of two-layer network, to reach hierarchical tasks to reduce probability of misidentification of high fall risk subjects, by establishing a borderline category using the rehabilitation labels, and extracting multi-scale spatio-temporal features. It was trained by using a wearable plantar pressure dataset collected from 48 elderly subjects. This method could achieve a real time fall risk identification accuracy of 73.27% by using only 9 gaits, which was superior to traditional methods. Moreover, the sensitivity reached 76.72%, proving its strength in identifying high risk samples. MhNet might be a promising way in real-time fall risk assessment for the elderly in their daily activities.
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