Human Identification for Activities of Daily Living: A Deep Transfer Learning Approach

Hongyi Zhu, Sagar Samtani, Hsinchun Chen, Jay F. Nunamaker

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)457-483
Number of pages27
JournalJournal of Management Information Systems
Issue number2
StatePublished - Apr 2 2020


  • Deep transfer learning
  • activities of daily living
  • deep learning
  • design science
  • health monitoring
  • human identification
  • mobile health

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management


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