%0 Conference Paper %1 jahn2017activity %A Jahn, Andreas %A Kroll, Dennis %A Lau, Sian Lun %A David, Klaus %B Proceedings of the IEEE Conference on Open Systems (ICOS) %C Miri, Sarawak, Malaysia %D 2017 %I IEEE %K comtec itegpub sociallink %P 1-5 %T An Activity History based Approach for Recognizing the Mode of Transportation %X By understanding a person's context, smart assistant systems aim to simplify and automate tasks. One type of useful context information is the mode of transportation. Commonly, the mode of transportation is recognized by analyzing raw sensor data of mobile devices, such as a smartphone. However, fine-grained sampling and collecting sensor data at high frequency to analyze raw sensor data is also conducted by activity recognition systems. These systems have been shown to accurately recognize activities such as sitting, standing, or walking. This means several systems analyze raw data for their needs. In our opinion, this doubled use of resources should be avoided. In this paper, we propose an approach, which avoids doubled raw sensor data analysis by taking advantage of the already available information of a person's activity. As a hypothesis, the approach assumes that observed activity sequences can lead to the mode of transportation. Thus, we aim to map activity sequences to modes of transportation. Applying the proposed approach, we show that the three different modes of transportation car, tram, and bus are recognized with an accuracy of up to 97%.

@inproceedings{jahn2017activity, abstract = {By understanding a person's context, smart assistant systems aim to simplify and automate tasks. One type of useful context information is the mode of transportation. Commonly, the mode of transportation is recognized by analyzing raw sensor data of mobile devices, such as a smartphone. However, fine-grained sampling and collecting sensor data at high frequency to analyze raw sensor data is also conducted by activity recognition systems. These systems have been shown to accurately recognize activities such as sitting, standing, or walking. This means several systems analyze raw data for their needs. In our opinion, this doubled use of resources should be avoided. In this paper, we propose an approach, which avoids doubled raw sensor data analysis by taking advantage of the already available information of a person's activity. As a hypothesis, the approach assumes that observed activity sequences can lead to the mode of transportation. Thus, we aim to map activity sequences to modes of transportation. Applying the proposed approach, we show that the three different modes of transportation car, tram, and bus are recognized with an accuracy of up to 97%.}, added-at = {2017-11-13T14:58:31.000+0100}, address = {Miri, Sarawak, Malaysia}, author = {Jahn, Andreas and Kroll, Dennis and Lau, Sian Lun and David, Klaus}, biburl = {https://www.bibsonomy.org/bibtex/25a462fe16611aaf05a45858f26d603de/comtec_pub}, booktitle = {Proceedings of the IEEE Conference on Open Systems (ICOS)}, interhash = {af3af5b01d4c02e397c253a7d8d12289}, intrahash = {5a462fe16611aaf05a45858f26d603de}, keywords = {comtec itegpub sociallink}, month = {November}, pages = {1-5}, publisher = {IEEE}, timestamp = {2017-11-15T11:38:19.000+0100}, title = {An {Activity} {History} based {Approach} for {Recognizing} the {Mode} of {Transportation}}, year = 2017 }