Kim Tae Hyong, Choi Ahnryul, Heo Hyun Mu, Kim Hyunggun, Mun Joung Hwan
Department of Biomechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 440-746, Korea.
Department of Biomedical Engineering, College of Medical Convergence, Catholic Kwandong University, 24 Beomilro 579 Beongil, Gangneung, Gangwon 25601, Korea.
Sensors (Basel). 2020 Oct 28;20(21):6126. doi: 10.3390/s20216126.
Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measurement factors that can affect the severity of an injury. It can be used as a design parameter for wearable protective devices to prevent injuries. In our study, a novel method is proposed to predict an impact acceleration magnitude after loss of balance using a single inertial measurement unit (IMU) sensor and a sequential-based deep learning model. Twenty-four healthy participants participated in this study for fall experiments. Each participant worn a single IMU sensor on the waist to collect tri-axial accelerometer and angular velocity data. A deep learning method, bi-directional long short-term memory (LSTM) regression, is applied to predict a fall's impact acceleration magnitude prior to fall impact (a fall in five directions). To improve prediction performance, a data augmentation technique with increment of dataset is applied. Our proposed model showed a mean absolute percentage error (MAPE) of 6.69 ± 0.33% with r value of 0.93 when all three different types of data augmentation techniques are applied. Additionally, there was a significant reduction of MAPE by 45.2% when the number of training datasets was increased by 4-fold. These results show that impact acceleration magnitude can be used as an activation parameter for fall prevention such as in a wearable airbag system by optimizing deployment process to minimize fall injury in real time.
撞击前跌倒检测可以在身体部位着地之前检测到跌倒。当它与保护系统集成时，可以直接防止因着地而造成的伤害。撞击加速度峰值大小是影响伤害严重程度的关键测量因素之一。它可以用作可穿戴防护设备预防伤害的设计参数。在我们的研究中，提出了一种新方法，使用单个惯性测量单元（IMU）传感器和基于序列的深度学习模型来预测失去平衡后的撞击加速度大小。24名健康参与者参与了这项跌倒实验研究。每位参与者在腰部佩戴一个IMU传感器，以收集三轴加速度计和角速度数据。应用一种深度学习方法，即双向长短期记忆（LSTM）回归，来预测跌倒撞击前（五个方向的跌倒）跌倒的撞击加速度大小。为了提高预测性能，应用了一种增加数据集的数据增强技术。当应用所有三种不同类型的数据增强技术时，我们提出的模型显示平均绝对百分比误差（MAPE）为6.69±0.33%，r值为0.93。此外，当训练数据集数量增加4倍时，MAPE显著降低了45.2%。这些结果表明，通过优化部署过程以实时最小化跌倒伤害，撞击加速度大小可以用作预防跌倒的激活参数，例如在可穿戴安全气囊系统中。
