USMART: an unsupervised semantic mining activity recognition technique

Juan Ye, Graeme Turnbull Stevenson, Simon Andrew Dobson

Research output: Contribution to journalArticlepeer-review

58 Citations (Scopus)


Recognising high-level human activities from low-level sensor data is a crucial driver for pervasive systems that wish to provide seamless and distraction-free support for users engaged in normal activities. Research in this area has grown alongside advances in sensing and communications, and experiments have yielded sensor traces coupled with ground truth annotations about the underlying environmental conditions and user actions. Traditional machine learning has had some success in recognising human activities; but the need for large volumes of annotated data and the danger of overfitting to specific conditions represent challenges in connection with the building of models applicable to a wide range of users, activities, and environments. We present USMART, a novel unsupervised technique that combines data- and knowledge-driven techniques. USMART uses a general ontology model to represent domain knowledge that can be reused across different environments and users, and we augment a range of learning techniques with ontological semantics to facilitate the unsupervised discovery of patterns in how each user performs daily activities. We evaluate our approach against four real-world third-party datasets featuring different user populations and sensor configurations, and we find that USMART achieves up to 97.5% accuracy in recognising daily activities.
Original languageEnglish
Article number16
Number of pages27
JournalACM Transactions on Intelligent Interaction Systems
Issue number4
Early online date13 Nov 2014
Publication statusPublished - Jan 2015


  • Activity recognition
  • Unsupervised learning
  • Ontologies
  • Clustering
  • String alignment
  • Sequential pattern
  • Segmentation
  • Pervasive computing
  • Smart home


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