Typical Behavior Patterns Extraction and Anomaly Detection Algorithm Based on Accumulated Home Sensor Data

  • Mori T
  • Fujii A
  • Shimosaka M
 et al. 
  • 16

    Readers

    Mendeley users who have this article in their library.
  • 21

    Citations

    Citations of this article.

Abstract

In this paper, we propose a method consists of two components, behavior patterns extraction and anomaly detection algorithm in daily life. To begin with, sensor data are accumulated in a room environment and behavior description labels are assigned for each data segment using HMM(hidden Markov model) and k-means method. An HMM is composed every day based on sensor data segments of the day. The behavior description label at each time segment is determined by likelihood of the segment computed using the HMM. In anomaly detection step, typical behavior sequences are acquired using probabilistic density of behavior occurrence and behavior successive time. Each probabilistic density is composed based on accumulating labeled- data using sequential discounting Laplace estimation and sequential discounting expectation and maximization algorithms. When a new datum comes, if typical behavior data change largely, the data is detected as anomaly. The proposed method is verified by a long-time activity detection sensor data taken at a house of elderly person.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Taketoshi Mori

  • Akinori Fujii

  • Masamichi Shimosaka

  • Hiroshi Noguchi

  • Tomomasa Sato

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free