Dynamic real-time segmentation and recognition of activities using a multi-feature windowing approach

Ahmad Shahi, Brendon J. Woodford, Hanhe Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)


Segmenting sensor events for activity recognition has many key challenges due to its unsupervised nature, the real-time requirements necessary for on-line event detection, and the possibility of having to recognise overlapping activities. A further challenge is to achieve robustness of classification due to sub-optimal choice of window size. In this paper, we present a novel real-time recognition framework to address these problems. The proposed framework is divided into two phases: off-line modeling and on-line recognition. In the off-line phase a representation called Activity Features (AFs) are built from statistical information about the activities from annotated sensory data and a Naïve Bayesian (NB) classifier is modeled accordingly. In the on-line phase, a dynamic multi-feature windowing approach using AFs and the learnt NB classifier is introduced to segment unlabeled sensor data as well as predicting the related activity. How this on-line segmentation occurs, even in the presence of overlapping activities, diverges from many other studies. Experimental results demonstrate that our framework can outperform the state-of-the-art windowing-based approaches for activity recognition involving datasets acquired from multiple residents in smart home test-beds.

Original languageEnglish
Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining
Subtitle of host publicationPAKDD 2017
EditorsU Kang, Ee-Peng Lim, Jeffrey Xu Yu, Yang-Sae Moon
Place of PublicationCham
Number of pages13
ISBN (Electronic)978-3-319-67274-8
ISBN (Print)978-3-319-67273-1
Publication statusPublished - 7 Oct 2017
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining - Jeju, Korea, Republic of
Duration: 23 May 201723 May 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10526 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st Pacific-Asia Conference on Knowledge Discovery and Data Mining
Abbreviated titlePAKDD 2017
Country/TerritoryKorea, Republic of


  • Classification
  • Human activity recognition
  • Machine learning
  • On-line stream mining
  • Real-time

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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