On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition
AbstractWith the recent spread of mobile devices equipped with different sensors, it is possible to continuously recognise and monitor activities in daily life. This sensor-based human activity recognition is formulated as sequence classification to categorise sequences of sensor values into appropriate activity classes. One crucial problem is how to model features that can precisely represent characteristics of each sequence and lead to accurate recognition. It is laborious and/or difficult to hand-craft such features based on prior knowledge and manual investigation about sensor data. To overcome this, we focus on a feature learning approach that extracts useful features from a large amount of data. In particular, we adopt a simple but effective one, called codebook approach, which groups numerous subsequences collected from sequences into clusters. Each cluster centre is called a codeword and represents a statistically distinctive subsequence. Then, a sequence is encoded as a feature expressing the distribution of codewords. The extensive experiments on different recognition tasks for physical, mental and eye-based activities validate the effectiveness, generality and usability of the codebook approach. View Full-Text
- Supplementary File 1:
ZIP-Document (ZIP, 2369 KB)
Share & Cite This Article
Shirahama, K.; Grzegorzek, M. On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition. Electronics 2017, 6, 44.
Shirahama K, Grzegorzek M. On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition. Electronics. 2017; 6(2):44.Chicago/Turabian Style
Shirahama, Kimiaki; Grzegorzek, Marcin. 2017. "On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition." Electronics 6, no. 2: 44.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.