Analysis of Smartphone-Ambient Data to Manage Daily Life Tasks
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2015
Typ der Arbeit
Master
Studiengang
Sammlung
Typ
11 - Studentische Arbeit
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Themenheft
DOI der Originalpublikation
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Reihe / Serie
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Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
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Verlag / Herausgebende Institution
Hochschule für Wirtschaft FHNW
Verlagsort / Veranstaltungsort
Olten
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Sometimes we feel overwhelmed by things we have to do and the things which keep us busy. In short our mind is flooded daily with information. In the information age we try to do more things in less time with the help of information and information technology – and we expect that technology becomes smarter and supports us. At the same time more and more people carry smartphones with them – and they fulfil multiple purposes such as being a pure communication tool or a personal digital diary.This thesis deals with the problem of task overload in the daily life of a single person. To address this problem, the potential of smartphone sensors to collect data about the person’s activities and their environment has been analysed.A significant research gap was detected in the area of mobile sensing and personal productivity and a clear need has been showed in the area of supporting people with management of their everyday tasks. As a result of this, a design science research methodology was chosen and an IT artefact on the basis of Android has been developed integrating different systems such as a sensing framework (SensingKit), machine learning libraries (Weka), gamified task management system (Habitica) and Google Services for activity and location recognition. The developed artefact has been evaluated using testing and descriptive methods (e.g. Proof-of-Concept). The results of the evaluation demonstrated that the artefact can in a given scenario detect certain activities and visited location of its users – and with a minimal manual task configuration – the detection of accomplished tasks work as well. The drawback of the evaluation was that certain specific activities need an in-depth machine learning training to provide more stable activity classification models – otherwise too many false positives will be reported. The developed system shows the potential of mobile sensing in the area of personal task management. Further long-term studies will be needed to validate the user acceptance and the long-term benefits.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Begutachtung
Open Access-Status
Lizenz
Zitation
MENON, Dilip, 2015. Analysis of Smartphone-Ambient Data to Manage Daily Life Tasks. Olten: Hochschule für Wirtschaft FHNW. Verfügbar unter: https://irf.fhnw.ch/handle/11654/39866