IEEE Trans Neural Syst Rehabil Eng. 2022;30:2385-2394. doi: 10.1109/TNSRE.2022.3199068. Epub 2022 Sep 1.
Proactively detecting falls and preventing injuries are among the primary keys to a healthy life for the elderly. Near-fall remote monitoring in daily life could provide key information to prevent future falls and obtain quantitative rehabilitation status for patients with weak balance ability. In this study, we developed a deep learning-based novel classification algorithm to precisely categorize three classes (falls, near-falls, and activities of daily living (ADLs)) using a single inertial measurement unit (IMU) device attached to the waist. A total of 34 young participants were included in this study. An IMU containing accelerometer and gyroscope sensors was fabricated to acquire acceleration and angular velocity signals. A comprehensive experiment including thirty-six types of activities (10 types of falls, 10 types of near-falls, and 16 types of ADLs) was designed based on previous studies. A modified directed acyclic graph-convolution neural network (DAG-CNN) architecture with hyperparameter optimization was proposed to predict fall, near-fall, and ADLs. Prediction results of the modified DAG-CNN structure were found to be approximately 7% more accurate than the traditional CNN structure. For the case of near-falls, the modified DAG-CNN demonstrated excellent prediction performance with accuracy of over 98% by combining gyroscope and accelerometer features. Additionally, by combining acceleration and angular velocity the trained model showed better performance than each model of acceleration and angular velocity. It is believed that information to preemptively handle the risk of falls and quantitatively evaluate the rehabilitation status of the elderly with weak balance will be provided by monitoring near-falls.
主动检测跌倒和预防伤害是老年人健康生活的关键。日常生活中的跌倒远程监测可以提供关键信息，以预防未来的跌倒，并为平衡能力较弱的患者获得定量的康复状况。在这项研究中，我们开发了一种基于深度学习的新型分类算法，使用附着在腰部的单个惯性测量单元（IMU）设备精确地对三个类别（跌倒、近跌和日常生活活动（ADL））进行分类。共有 34 名年轻参与者参与了这项研究。制作了一个包含加速度计和陀螺仪传感器的 IMU，以获取加速度和角速度信号。根据以往的研究，设计了一个包含 36 种活动（10 种跌倒、10 种近跌和 16 种 ADL）的综合实验。提出了一种具有超参数优化的改进有向无环图卷积神经网络（DAG-CNN）架构，以预测跌倒、近跌和 ADL。与传统的 CNN 结构相比，改进的 DAG-CNN 结构的预测结果被发现大约准确 7%。对于近跌情况，改进的 DAG-CNN 通过结合陀螺仪和加速度计特征，表现出了出色的预测性能，准确率超过 98%。此外，通过结合加速度和角速度，训练后的模型表现出比加速度和角速度的每个模型更好的性能。通过监测近跌，可以为提前处理跌倒风险和定量评估平衡能力较弱的老年人的康复状况提供信息。
