Scalable recognition of daily activities with wearable sensors

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.