Machine Learning-Based Pre-Impact Fall Detection Model to Discriminate Various Types of Fall
Machine Learning-Based Pre-Impact Fall Detection Model to Discriminate Various Types of Fall
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DOI：
10.1115/1.4043449
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发表时间：
2019-08-01
影响因子：
1.7
通讯作者：
Mun, Joung Hwan
中科院分区：
文献类型：
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作者：
Kim, Tae Hyong;Choi, Ahnryul;Mun, Joung Hwan
撞击前跌倒检测可以更快地发送警报服务，以减少长时间躺在床上的情况，并降低住院治疗的风险。检测各种类型的跌倒以在撞击之前确定撞击部位或方向是重要的，因为它增加了降低跌倒相关伤害的发生率或严重程度的机会。在这项研究中，一个强大的碰撞前跌倒检测模型的开发，分类各种活动和福尔斯作为多类和它的性能与以前开发的模型的性能进行了比较。12名健康受试者参加了本研究。要求所有受试者将惯性测量单元模块固定在左髂嵴附近的腰带上，以收集每次活动的加速度计数据。我们提出的新模型包括特征计算和无限潜在特征选择（ILFS）算法，活动的自动标记，以及离散和连续时间序列数据的机器学习分类器的应用。九个机器学习分类器被应用于检测福尔斯之前的影响，并得出最终的检测结果排序的分类器。我们的模型表现出最高的分类准确率。结果表明，与以前的模型相比，所提出的模型可以分类为多类，离散数据分类器的平均分类准确率为99.57 +/- 0.01%，连续时间序列分类器的平均分类准确率为99.84 +/- 0.02%（p
Pre-impact fall detection can send alarm service faster to reduce long-lie conditions and decrease the risk of hospitalization. Detecting various types of fall to determine the impact site or direction prior to impact is important because it increases the chance of decreasing the incidence or severity of fall-related injuries. In this study, a robust pre-impact fall detection model was developed to classify various activities and falls as multiclass and its performance was compared with the performance of previous developed models. Twelve healthy subjects participated in this study. All subjects were asked to place an inertial measuring unit module by fixing on a belt near the left iliac crest to collect accelerometer data for each activity. Our novel proposed model consists of feature calculation and infinite latent feature selection (ILFS) algorithm, auto labeling of activities, and application of machine learning classifiers for discrete and continuous time series data. Nine machine-learning classifiers were applied to detect falls prior to impact and derive final detection results by sorting the classifier. Our model showed the highest classification accuracy. Results for the proposed model that could classify as multiclass showed significantly higher average classification accuracy of 99.57 +/- 0.01% for discrete data-based classifiers and 99.84 +/- 0.02% for continuous time series-based classifiers than previous models (p