A survey on anomalous behavior detection for elderly care using dense-sensing networks

Samundra Deep, Xi Zheng, Chandan Karmakar, Dongjin Yu, Leonard Hamey, Jiong Jin

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


Facing the gradual ageing society, elderly people living independently are in need of serious attention. In order to assist them to live in a safer environment, the increasing cost of nursing care and the shortage of health-care workers urges the demand of home-based assisted living in recent times. Therefore, home-based health-care has become an active research domain, particularly the abnormal activities detection involving information and communications technologies. This survey paper highlights this kind of technologies that exist for human anomalous behavior detection. It also reviews and discusses the current research trends, their outcomes and effects in elderly care. Our study is mainly focused on dense sensing network based activities and anomaly detection, which are robust to environment change, non-intrusive, user-friendly in the sense that do not require the occupant to wear any devices. From our study, we observe that employing sensor fusion techniques could significantly increases the efficiency of dense sensing network. In addition, sensor fusion models ensure a high level of robustness and effectiveness compared to the traditional methods.
Original languageEnglish
Pages (from-to)352-370
Number of pages19
JournalIEEE Communications Surveys and Tutorials
Issue number1
Publication statusPublished - 2020


  • Anomaly detection
  • dense sensing
  • activity recognition
  • sensor fusion


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