Scalable recognition of daily activities with wearable sensors

  • Authors:
  • Tâm Huỳnh;Ulf Blanke;Bernt Schiele

  • Affiliations:
  • Computer Science Department, TU Darmstadt, Germany;Computer Science Department, TU Darmstadt, Germany;Computer Science Department, TU Darmstadt, Germany

  • Venue:
  • LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
  • Year:
  • 2007

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Abstract

High-level and longer-term activity recognition has great potentials in areas such as medical diagnosis and human behavior modeling. So far however, activity recognition research has mostly focused on lowlevel and short-term activities. This paper therefore makes a first step towards recognition of high-level activities as they occur in daily life. For this we record a realistic 10h data set and analyze the performance of four different algorithms for the recognition of both low- and high-level activities. Here we focus on simple features and computationally efficient algorithms as this facilitates the embedding and deployment of the approach in real-world scenarios. While preliminary, the experimental results suggest that the recognition of high-level activities can be achieved with the same algorithms as the recognition of low-level activities.