User-centered fall detection using supervised, on-line learning and transfer learning
User-centered fall detection using supervised, on-line learning and transfer learning
复制标题
使用监督在线学习和迁移学习以用户为中心的跌倒检测
DOI：
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发表时间：
2019
影响因子：
4.2
通讯作者：
J. Sedano
中科院分区：
文献类型：
--
作者：
J. Villar;Enrique A. de la Cal;M. Fáñez;Víctor M. González;J. Sedano
没有必要解释跌倒检测（FD）在老年人群中的相关性：他们接受帮助的速度越快，康复的概率就越高。FD已经被广泛研究，并且在文献中已经提出了许多解决方案，包括具有知名商标的商业产品。基本上，大多数解决方案包括事件检测方法，然后是特征提取块，作为分类器的输入，将样本标记为Fall或Not Fall。然而，由于误报率相对较高，这些建议总是依赖于用户反馈。本研究提出了两个监督的用户为中心的解决方案FD使用两个公开的数据集，包括模拟福尔斯与放置在手腕上的感觉系统进行评估。在这项研究中，我们专注于为每个用户学习特定的模型，而不是专注于学习一个通用的模型。为了克服缺乏数据从秋天类，一个在线学习方法来分组的事件所产生的日常生活的活动和随之而来的分析如何跌倒事件，然后可以检测到。此外，迁移学习阶段从先前的经验中产生先验知识，这些先验知识可以被引入到在线学习分类器中以提高其性能。在两个公开的数据集上进行了完整的实验，分析了监督分类器的性能，以及由跌倒检测和分类器组成的整个检测系统的性能。在在线学习的情况下，只包括分类器的结果，无论是否有迁移学习。结果表明，FD用户为中心的解决方案有能力适应特定用户的信息，主要是在使用在线学习和迁移学习。然而，迁移学习阶段需要改进，以避免这可能带来的复杂性。
There is no need to explain the relevance of fall detection (FD) in the elderly population: the faster they receive help, the higher the probabilities of recovery. FD has been widely studied, and a number of solutions have been proposed in the literature, including commercial products with well-known trademarks. Basically, the majority of the solutions include an Event Detection method followed by a feature extraction block as the inputs to a classifier that labels the sample as Fall or as Not Fall. Nevertheless, the proposals always rely on user feedback because of the relatively high percentage of false positives. This study proposes two supervised user-centered solutions for FD which are evaluated using two publicly available data sets that include simulated falls with the sensory system placed on a wrist. Instead of focusing on learning a generalized model, in this study we focus on learning specific models for each user. To overcome the lack of data from the Fall class, an on-line learning method to group the events arising from the activities of daily life and the consequent analysis of how fall events can then be detected is included. In addition, a transfer learning stage produces a priori knowledge from previous experiences that can be introduced into the on-line learning classifier in order to enhance its performance. Complete experimentation is carried out on two publicly available data sets, analyzing the performance of the supervised classifiers, and the performance of the whole detection system composed of the fall detection plus the classifier. In the case of on-line learning, only the results for the classifier, either with or without transfer learning, are included. The results suggest that FD user-centered solutions have the capacity to adapt to the information of a specific user, mostly when using both on-line learning and transfer learning. Nevertheless, the transfer learning stage needs refinements in order to avoid the complexity that this might introduce.
作为年龄函数的健康非跌倒成年人的步态和平衡的客观测量。
影响因子：
2.4
作者：
Virmani,Tuhin;Gupta,Harsh;Shah,Jesal;Larson-Prior,Linda
通讯作者：
Larson-Prior,Linda
影响因子：
3.2
作者：
Dugan SA;Gabriel KP;Lange-Maia BS;Karvonen-Gutierrez C
通讯作者：
Karvonen-Gutierrez C