Human activity recognition (HAR) is a growing area within machine learning as the possible applications are vast, especially with the growing amount of collectable sensor data as Internet of Things-devices are becoming more accessible. This project aims to contribute to HAR by developing two supervised machine learning algorithms that are able to distinguish between four different human activities. We collected data from the tri-axial accelerometer in two different smartphones while doing these activities, and put together a dataset. The algorithms that were used was a convolutional neural network (CNN) and a support vector machine (SVM), and they were applied to the dataset separately. The results show that it is possible to accurately classify the activities using the algorithms and that a short time window of 3 seconds is enough to classify the activities with an accuracy of over 99% with both algorithms. The SVM outperformed the CNN slightly. We also discuss the result and continuations of this study. / Mlinsklig aktivitetsigenkanning (HAR) lir ett vlixande omrade inom maskininllirning da de mojliga applikationerna lir stora, speciellt med den vlixande mangd insamlingsbar sensordata da Internet of Things-enheter blir mer atkomliga. Detta projekt siktar pa att bidra till HAR genom att utveckla tva algoritmer som kan urskilja mellan fyra olika mlinskliga aktiviteter. Vi samlade in data fran den treaxlade accelerometern i tva olika smarta telefoner medans dessa aktiviteter utfordes, och satte ihop ett dataset. Algoritmerna som anvlindes var ett faltande neuralt nlitverk och en stodvektormaskin, och de applicerades separat pa datasetet. Resultaten visar att det lir mojligt att med slikerhet klassificera aktiviteterna genom att anvlinda dessa algoritmer och att ett kort tidsfonster med 3 sekunder av data lir tillrlickligt for att klassificera med en slikerhet pa over 99% med bada algoritmerna. Stodvektormaskinen presterade nagot blittre an det neurala nlitverket. Vi diskuterar liven resultatet och fortsatta studier. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-308478 |
| Date | January 2021 |
| Creators | Svensson, Patrik, Wendel, Erik |
| Publisher | KTH, Skolan för elektroteknik och datavetenskap (EECS) |
| 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 |
| Relation | TRITA-EECS-EX ; 2021:178 |
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