Human Identification for Activities of Daily Living: A Deep Transfer Learning Approach
Human Identification for Activities of Daily Living: A Deep Transfer Learning Approach
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DOI：
10.1080/07421222.2020.1759961
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发表时间：
2020-04
影响因子：
7.7
通讯作者：
Hongyi Zhu;Sagar Samtani;Hsinchun Chen;J. Nunamaker
中科院分区：
文献类型：
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作者：
Hongyi Zhu;Sagar Samtani;Hsinchun Chen;J. Nunamaker
基于传感器的家庭日常生活活动（ADL）监测系统已经出现，以远程监测老年人的自我护理能力。然而，不显眼的，隐私友好的基于对象运动传感器的系统面临的挑战，如稀缺的标记数据和ADL表演者在多居民设置混乱。本研究采用设计科学范式，开发了一个创新的人类识别深度迁移学习框架（DTL-HID），以应对这两个挑战。提出了一种新的卷积神经网络（CNN）自动提取DTL-HID框架的全面的时间和跨轴运动模式。我们严格评估DTL-HID框架对国家的最先进的基准（例如，k最近邻、支持向量机和替代CNN设计）。结果表明，我们提出的DTL-HID框架可以准确地识别ADL表演者，即使在少量的标记数据。我们展示了一个案例研究，并讨论了利益相关者如何进一步将这种方法应用于老年人的不显眼的智能家居监控。除了展示框架的实际效用，我们还讨论了我们的设计原则对移动的分析和设计科学研究的两个影响：（1）提取时间和轴向局部依赖关系可以从多轴时间序列数据中捕获更丰富的信息，以及（2）转移在具有足够数据的相关源域上学到的知识可以提高目标域上所需任务的性能。
ABSTRACT Sensor-based home Activities of Daily Living (ADLs) monitoring systems have emerged to monitor elderly people’s self-care ability remotely. However, the unobtrusive, privacy-friendly object motion sensor-based systems face challenges such as scarce labeled data and ADL performer confusion in a multi-resident setting. This study adopts the design science paradigm to develop an innovative deep transfer learning framework for human identification (DTL-HID) to address both challenges. A novel convolutional neural network (CNN) is proposed to automatically extract comprehensive temporal and cross-axial motion patterns for the DTL-HID framework. We rigorously evaluate the DTL-HID framework against state-of-the-art benchmarks (e.g., k Nearest Neighbors, Support Vector Machines, and alternative CNN designs). Results demonstrate our proposed DTL-HID framework can identify the ADL performer accurately even on a small amount of labeled data. We demonstrate a case study and discuss how stakeholders can further apply this approach to unobtrusive smart home monitoring for senior citizens. Beyond demonstrating the framework’s practical utility, we discuss two implications of our design principles to mobile analytics and design science research: (1) extracting temporal and axial local dependencies can capture richer information from multi-axial time-series data and (2) transferring knowledge learned on a relevant source domain with sufficient data can improve the performance of the desired task on the target domain with scarce data.