Yu Xiaoqun, Qiu Hai, Xiong Shuping
Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
CETHIK Group Corporation Research Institute, Hangzhou, China.
Front Bioeng Biotechnol. 2020 Feb 12;8:63. doi: 10.3389/fbioe.2020.00063. eCollection 2020.
Falls in the elderly is a major public health concern due to its high prevalence, serious consequences and heavy burden on the society. Many falls in older people happen within a very short time, which makes it difficult to predict a fall before it occurs and then to provide protection for the person who is falling. The primary objective of this study was to develop deep neural networks for predicting a fall during its initiation and descending but before the body impacts to the ground so that a safety mechanism can be enabled to prevent fall-related injuries. We divided the falling process into three stages (non-fall, pre-impact fall and fall) and developed deep neutral networks to perform three-class classification. Three deep learning models, convolutional neural network (CNN), long short term memory (LSTM), and a novel hybrid model integrating both convolution and long short term memory (ConvLSTM) were proposed and evaluated on a large public dataset of various falls and activities of daily living (ADL) acquired with wearable inertial sensors (accelerometer and gyroscope). Fivefold cross validation results showed that the hybrid ConvLSTM model had mean sensitivities of 93.15, 93.78, and 96.00% for non-fall, pre-impact fall and fall, respectively, which were higher than both LSTM (except the fall class) and CNN models. ConvLSTM model also showed higher specificities for all three classes (96.59, 94.49, and 98.69%) than LSTM and CNN models. In addition, latency test on a microcontroller unit showed that ConvLSTM model had a short latency of 1.06 ms, which was much lower than LSTM model (3.15 ms) and comparable with CNN model (0.77 ms). High prediction accuracy (especially for pre-impact fall) and low latency on the microboard indicated that the proposed hybrid ConvLSTM model outperformed both LSTM and CNN models. These findings suggest that our proposed novel hybrid ConvLSTM model has great potential to be embedded into wearable inertial sensor-based systems to predict pre-impact fall in real-time so that protective devices could be triggered in time to prevent fall-related injuries for older people.
老年人跌倒因其高发生率、严重后果及沉重的社会负担，成为一个重大的公共卫生问题。许多老年人跌倒在极短时间内发生，这使得在跌倒发生前难以预测，进而难以对跌倒者提供保护。本研究的主要目标是开发深度神经网络，用于在跌倒起始和下降阶段、但身体尚未撞击地面之前预测跌倒，以便启用安全机制来预防与跌倒相关的伤害。我们将跌倒过程分为三个阶段（非跌倒、撞击前跌倒和跌倒），并开发深度神经网络进行三类分类。提出了三种深度学习模型，即卷积神经网络（CNN）、长短期记忆网络（LSTM）以及一种融合卷积和长短期记忆的新型混合模型（ConvLSTM），并在一个通过可穿戴惯性传感器（加速度计和陀螺仪）获取的包含各种跌倒及日常生活活动（ADL）的大型公共数据集上进行评估。五折交叉验证结果表明，混合ConvLSTM模型对非跌倒、撞击前跌倒和跌倒的平均敏感度分别为93.15%、93.78%和96.00%，高于LSTM（跌倒类别除外）和CNN模型。ConvLSTM模型对所有三个类别的特异性（96.59%、94.49%和98.69%）也高于LSTM和CNN模型。此外，在微控制器单元上进行的延迟测试表明，ConvLSTM模型的延迟较短，为1.06毫秒，远低于LSTM模型（3.15毫秒），与CNN模型（0.77毫秒）相当。微板上的高预测准确率（尤其是对于撞击前跌倒）和低延迟表明，所提出的混合ConvLSTM模型优于LSTM和CNN模型。这些发现表明，我们提出的新型混合ConvLSTM模型具有很大潜力被嵌入到基于可穿戴惯性传感器的系统中，以实时预测撞击前跌倒，从而及时触发保护装置，防止老年人发生与跌倒相关的伤害。
