A general framework for sensor-based human activity recognition.

TitelA general framework for sensor-based human activity recognition.
Publication TypeJournal Article
Year of Publication2018
AuthorsKöping L., Shirahama K., Grzegorzek M.
JournalComputers in biology and medicine
Volume95
Pages248-260
Date Published2018 04 01
Publication Languageeng
ISSN1879-0534
Abstract

Today's wearable devices like smartphones, smartwatches and intelligent glasses collect a large amount of data from their built-in sensors like accelerometers and gyroscopes. These data can be used to identify a person's current activity and in turn can be utilised for applications in the field of personal fitness assistants or elderly care. However, developing such systems is subject to certain restrictions: (i) since more and more new sensors will be available in the future, activity recognition systems should be able to integrate these new sensors with a small amount of manual effort and (ii) such systems should avoid high acquisition costs for computational power. We propose a general framework that achieves an effective data integration based on the following two characteristics: Firstly, a smartphone is used to gather and temporally store data from different sensors and transfer these data to a central server. Thus, various sensors can be integrated into the system as long as they have programming interfaces to communicate with the smartphone. The second characteristic is a codebook-based feature learning approach that can encode data from each sensor into an effective feature vector only by tuning a few intuitive parameters. In the experiments, the framework is realised as a real-time activity recognition system that integrates eight sensors from a smartphone, smartwatch and smartglasses, and its effectiveness is validated from different perspectives such as accuracies, sensor combinations and sampling rates.

DOI10.1016/j.compbiomed.2017.12.025
PubMed Link

http://www.ncbi.nlm.nih.gov/pubmed/29361267?dopt=Abstract

Alternate JournalComput. Biol. Med.
Erstellt am 15. Oktober 2018 - 10:11.

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