Quantified Self & Mobile Health Monitoring | Aleksi Elias Pertola
| | Abstract | The rapid evolution of mobile phones from mere vessels for voice- and text-based communication to personal assistants with a treasure chest of advanced features has created a new paradigm in mobile applications. The computational power and ubiquity of such devices has generated plenty of interest from the healthcare sector, particularly via the recent parallel development of body worn sensors that can communicate with them wirelessly. There is a strong incentive, both from an economic and an altruistic perspective, to develop mobile applications that enable self-help, patient homecare, and remote disease management. However, very little research exists at present in the field of affecting health change behavior in people through such mobile applications.
This thesis proposes a body worn sensor-based mobile platform for the physiotherapeutic treatment of patients suffering from specific back disorders and back pain at home. In particular, this thesis focuses on developing a user interface characterized by persuasive, encouraging and motivational aspects that ensure adherence and compliance with respect to physiotherapeutic exercise regimes prescribed by a physiotherapist. | | Type | Master's thesis [Academic thesis] | | Year | 2013 | | Publisher | Technical University of Denmark, DTU Compute, E-mail: compute@compute.dtu.dk | | Address | Matematiktorvet, Building 303-B, DK-2800 Kgs. Lyngby, Denmark | | Series | IMM-M.Sc.-2013-08 | | Note | DTU supervisor: Michael Kai Petersen, mkp@imm.dtu.dk, DTU Compute | | Electronic version(s) | [pdf] | | Publication link | http://www.compute.dtu.dk/English.aspx | | BibTeX data | [bibtex] | | IMM Group(s) | Intelligent Signal Processing |
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