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Collection of a Diverse, Realistic and Annotated Dataset for Wearable Activity Recognition
School of Computing, Ulster University, Co. Antrim, Northern Ireland, United Kingdom.
School of Computing, Ulster University, Co. Antrim, Northern Ireland, United Kingdom.
School of Computing, Ulster University, Co. Antrim, Northern Ireland, United Kingdom.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0003-3191-8335
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2018 (Engelska)Ingår i: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018, IEEE, 2018, s. 555-560, artikel-id 8480322Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper discusses the opportunities and challenges associated with the collection of a large scale, diverse dataset for Activity Recognition. The dataset was collected by 141 undergraduate students, in a controlled environment. Students collected triaxial accelerometer data from a wearable accelerometer whilst each carrying out 3 of the 18 investigated activities, categorized into 6 scenarios of daily living. This data was subsequently labelled, anonymized and uploaded to a shared repository. This paper presents an analysis of data quality, through outlier detection and assesses the suitability of the dataset for the creation and validation of Activity Recognition models. This is achieved through the application of a range of common data driven machine learning approaches. Finally, the paper describes challenges identified during the data collection process and discusses how these could be addressed. Issues surrounding data quality, in particular, identifying and addressing poor calibration of the data were identified. Results highlight the potential of harnessing these diverse data for Activity Recognition. Based on a comparison of six classification approaches, a Random Forest provided the best classification (F-measure: 0.88). In future data collection cycles, participants will be encouraged to collect a set of 'common' activities, to support generation of a larger homogeneous dataset. Future work will seek to refine the methodology further and to evaluate model on new unseen data.

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IEEE, 2018. s. 555-560, artikel-id 8480322
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URN: urn:nbn:se:ltu:diva-72319DOI: 10.1109/PERCOMW.2018.8480322Scopus ID: 2-s2.0-85056470379ISBN: 978-1-5386-3227-7 (digital)OAI: oai:DiVA.org:ltu-72319DiVA, id: diva2:1272240
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2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 19-23 March 2018, Athens, Greece
Tillgänglig från: 2018-12-18 Skapad: 2018-12-18 Senast uppdaterad: 2018-12-18Bibliografiskt granskad

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Hallberg, Josef

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