Personalized Fall Detection System
Personalized Fall Detection System
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DOI：
10.1109/percomworkshops48775.2020.9156172
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
A. Ngu;V. Metsis;Shauna Coyne;Brian Chung;Rachel Pai;Joshua Chang
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文献类型：
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作者：
A. Ngu;V. Metsis;Shauna Coyne;Brian Chung;Rachel Pai;Joshua Chang
本文探讨了使用深度神经网络与集成技术相结合训练的基于智能手表的跌倒检测模型的个性化。深度神经网络在用于跌倒检测时面临实际挑战，通常倾向于具有有限的训练样本和不平衡的数据集。此外，腕表产生的许多运动可能会被误认为是跌倒。由于跌倒是罕见事件，因此不可能获得大量真实世界的标记跌倒数据。然而，从用户那里收集大量的非跌倒数据样本是很容易的。在本文中，我们的目标是通过首先训练一个通用的深度学习集成模型，优化高召回率，然后通过从个人用户收集个性化的假阳性样本，通过SmartFall应用程序的反馈。我们执行了真实的-世界上五个志愿者的实验，并得出结论，个性化的跌倒检测模型显着优于通用跌倒检测模型，特别是在精度方面。我们通过使用一种新的度量标准来评估模型的准确性，并通过将假阳性率与随时间推移的加速度峰值的数量进行归一化，进一步验证了个性化的性能。
This paper explores the personalization of smartwatch-based fall detection models trained using a combination of deep neural networks with ensemble techniques. Deep neural networks face practical challenges when used for fall detection, which in general tend to have limited training samples and imbalanced datasets. Moreover, many motions generated by a wrist-worn watch can be mistaken for a fall. Obtaining a large amount of real-world labeled fall data is impossible as fall is a rare event. However, it is easy to collect a large number of non-fall data samples from users. In this paper, we aim to mitigate the scarcity of training data in fall detection by first training a generic deep learning ensemble model, optimized for high recall, and then enhancing the precision of the model, by collecting personalized false positive samples from individual users, via feedback from the SmartFall App. We performed real-world experiments with five volunteers and concluded that a personalized fall detection model significantly outperforms generic fall detection models, especially in terms of precision. We further validated the performance of personalization by using a new metric for evaluating the accuracy of the model via normalizing false positive rates with regard to the number of spikes of acceleration over time.