PDF(Pubmed)
Abstract：
BACKGROUND: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors-based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning-based FDSs using manual feature extraction, and deep learning (DL)-based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy.
OBJECTIVE: This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities.
METHODS: Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model.
RESULTS: The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%).
CONCLUSIONS: This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.
OBJECTIVE: This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities.
METHODS: Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model.
RESULTS: The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%).
CONCLUSIONS: This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.
摘要：
背景：跌倒检测对保障人类健康具有重要意义。通过监测运动数据，跌倒检测系统(FDS)可以检测跌倒事故。最近,基于可穿戴传感器的FDSs已经成为研究的主流,可以使用经验将其分类为基于阈值的FDS，使用手动特征提取的基于机器学习的FDSs，和使用自动特征提取的基于深度学习(DL)的FDS。然而,大多数FDSS专注于传感器数据的全球信息，忽略了数据的不同部分对跌倒检测的贡献不同的事实。这个缺点使得FDSs很难准确区分实际跌倒和类似跌倒的动作的相似人类运动模式，导致检测精度下降。
目的：本研究旨在开发和验证DL框架，以使用来自可穿戴传感器的加速度和陀螺仪数据来准确检测跌倒。我们旨在探索从传感器数据中提取的基本贡献特征，以区分跌倒与日常生活活动。这项研究的意义在于通过使用DL方法设计加权特征表示来改革FDS，以有效区分跌倒事件和跌倒样活动。
方法：基于3轴加速度和陀螺仪数据，我们提出了一种新的DL架构，双流卷积神经网络自注意(DSCS)模型。与以往的研究不同,所使用的架构可以从加速度和陀螺仪数据中提取全局特征信息。此外,我们加入了一个自我注意模块，为原始特征向量分配不同的权重，使模型能够学习传感器数据的贡献效应,提高分类精度。所提出的模型在2个公共数据集上进行了训练和测试：SisFall和MobiFall。此外,招募了10名参与者对DSCS模型进行实际验证。总共进行了1700次试验来测试模型的泛化能力。
结果：在SisFall和MobiFall的测试集上，DSCS模型的跌倒检测准确率分别为99.32％（召回率=99.15％；精度=98.58％）和99.65％（召回率=100％；精度=98.39％），分别。在消融实验中,我们将DSCS模型与最先进的机器学习和DL模型进行了比较。在SisFall数据集上,DSCS模型达到了第二好的精度；在MobiFall数据集上，DSCS模型取得了最好的精度，召回，和精度。在实际验证中，DSCS模型的准确率为96.41%(召回率=95.12%;特异性=97.55%).
结论：这项研究表明，DSCS模型可以在2个公开可用的数据集上显着提高跌倒检测的准确性，并且在实际验证中表现强劲。
目的：本研究旨在开发和验证DL框架，以使用来自可穿戴传感器的加速度和陀螺仪数据来准确检测跌倒。我们旨在探索从传感器数据中提取的基本贡献特征，以区分跌倒与日常生活活动。这项研究的意义在于通过使用DL方法设计加权特征表示来改革FDS，以有效区分跌倒事件和跌倒样活动。
方法：基于3轴加速度和陀螺仪数据，我们提出了一种新的DL架构，双流卷积神经网络自注意(DSCS)模型。与以往的研究不同,所使用的架构可以从加速度和陀螺仪数据中提取全局特征信息。此外,我们加入了一个自我注意模块，为原始特征向量分配不同的权重，使模型能够学习传感器数据的贡献效应,提高分类精度。所提出的模型在2个公共数据集上进行了训练和测试：SisFall和MobiFall。此外,招募了10名参与者对DSCS模型进行实际验证。总共进行了1700次试验来测试模型的泛化能力。
结果：在SisFall和MobiFall的测试集上，DSCS模型的跌倒检测准确率分别为99.32％（召回率=99.15％；精度=98.58％）和99.65％（召回率=100％；精度=98.39％），分别。在消融实验中,我们将DSCS模型与最先进的机器学习和DL模型进行了比较。在SisFall数据集上,DSCS模型达到了第二好的精度；在MobiFall数据集上，DSCS模型取得了最好的精度，召回，和精度。在实际验证中，DSCS模型的准确率为96.41%(召回率=95.12%;特异性=97.55%).
结论：这项研究表明，DSCS模型可以在2个公开可用的数据集上显着提高跌倒检测的准确性，并且在实际验证中表现强劲。
