The rapid development of sensor technology in smartphone and wearable devices has led research to the area of human activity recognition (HAR). As a phase in HAR, applying classification models to collected sensor data is well-researched, and many of the different models can recognize activities successfully. Furthermore, some methods give successful results only using one or two sensors. The use of HAR within pain management is also an existing research field, but applying HAR to the pain treatment strategy of acceptance and commitment therapy (ACT) is not well-documented. The relevance of HAR in this context is that ACT:s core ideas are based on the perspective that daily life activities are connected to pain. In this thesis, state-of-the-art examples for sensor-based HAR applicable to ACT are provided through a literature review. Based on these findings, the practical use is assessed in order to provide a perspective to the current state of research.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-43459 |
| Date | January 2021 |
| Creators | Hansson, Hampus, Gyllström, Martin |
| Publisher | Malmö universitet, Fakulteten för teknik och samhälle (TS) |
| 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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