Griškevičius Julius, Daunoravičienė Kristina, Petrauskas Liudvikas, Apšega Andrius, Alekna Vidmantas
Department of Biomechanical Engineering, Vilnius Gediminas Technical University, LT-10105 Vilnius, Lithuania.
Faculty of Medicine, Vilnius University, LT-03101 Vilnius, Lithuania.
Sensors (Basel). 2025 May 26;25(11):3351. doi: 10.3390/s25113351.
Frailty is a common syndrome in the elderly, marked by an increased risk of negative health outcomes such as falls, disability and death. It is important to detect frailty early and accurately to apply timely interventions that can improve health results in older adults. Traditional evaluation methods often depend on subjective evaluations and clinical opinions, which might lack consistency. This research uses deep learning to classify frailty from spectrograms based on IMU data collected during gait analysis. The study retrospectively analyzed an existing IMU dataset. Gait data were categorized into Frail, PreFrail, and NoFrail groups based on clinical criteria. Six IMUs were placed on lower extremity segments to collect motion data during walking activities. The raw signals from accelerometers and gyroscopes were converted into time-frequency spectrograms. A convolutional neural network (CNN) trained solely on raw IMU-derived spectrograms achieved 71.4 % subject-wise accuracy in distinguishing frailty levels. Minimal preprocessing did not improve subject-wise performance, suggesting that the raw time-frequency representation retains the most salient gait cues. These findings suggest that wearable sensor technology combined with deep learning provides a robust, objective tool for frailty assessment, offering potential for clinical and remote health monitoring applications.
衰弱是老年人中常见的一种综合征，其特征是出现跌倒、残疾和死亡等不良健康后果的风险增加。早期准确检测衰弱情况对于及时采取干预措施以改善老年人的健康状况非常重要。传统的评估方法往往依赖主观评估和临床意见，可能缺乏一致性。本研究利用深度学习，根据步态分析期间收集的惯性测量单元（IMU）数据，从频谱图中对衰弱进行分类。该研究对一个现有的IMU数据集进行了回顾性分析。根据临床标准，将步态数据分为衰弱组、衰弱前期组和非衰弱组。在下肢段放置了六个IMU，以收集行走活动期间的运动数据。来自加速度计和陀螺仪的原始信号被转换为时频频谱图。仅基于原始IMU衍生频谱图训练的卷积神经网络（CNN）在区分衰弱水平方面达到了71.4%的个体准确率。最少的预处理并未提高个体表现，这表明原始时频表示保留了最显著的步态线索。这些发现表明，可穿戴传感器技术与深度学习相结合，为衰弱评估提供了一种强大、客观的工具，为临床和远程健康监测应用提供了潜力。
