Mobile phones can be used to assess patients health by collecting valuable informationthrough the sensors, GPS and accelerometers and then uploading them to a centraldatabase to allow for clinicians to remotely monitor the decline, improvement or over-all health status of a patient [1] [2].Many mHealth applications use mobile phones built-in GPS, accelerometer and othersensors which allows for a large selection of work to compare the implemented exercisecapacity test to [1].The exercise capacity tests developed for this thesis is to be used in Mobistudy. Mobis-tudy is an open mobile-health platform for clinical research. The platform has an emphasison regulatory compliance, patient consent and transparency [3].The thesis resulted in the creation of two artifacts which were able to successfullycollect data from the user to transfer to the clinicians using the application. During theanalysis it was found that the SMWT algorithm developed by Salvi et al [4] worked wellunder non optimal conditions. The Queens College Step Tests result were in general poor,however more testing with more different phones is required to provide a clear answer.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-20795 |
| Date | January 2020 |
| Creators | Forsnor, Elin, Morau, Felix |
| Publisher | Malmö universitet, Fakulteten för teknik och samhälle (TS), Malmö universitet, Fakulteten för teknik och samhälle (TS), Malmö universitet/Teknik och samhälle |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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