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 low-level 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.
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Huỳnh, T., Blanke, U., Schiele, B. (2007). Scalable Recognition of Daily Activities with Wearable Sensors. In: Hightower, J., Schiele, B., Strang, T. (eds) Location- and Context-Awareness. LoCA 2007. Lecture Notes in Computer Science, vol 4718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75160-1_4
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DOI: https://doi.org/10.1007/978-3-540-75160-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75159-5
Online ISBN: 978-3-540-75160-1
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