This thesis projects presents a system for gathering and using data in the context of a smarthome research enviroment. The system was developed at the Human Health and ActivityLaborty, H2Al, at Luleå University of Technology and consists of two distinct parts. First, a data export application that runs in the H2Al enviroment. This application syn-chronizes data from various sensor systems and forwards the data for further analysis. Thisanalysis was performed in the iMotions platform in order to visualize, record and export data.As a delimitation, the only sensor used was the WideFind positional system installed at theH2Al. Secondly, an activity recognition application that uses data generated from the iMotionsplatform and data export application. This includes several scripts which transforms rawdata into labeled datasets and translates them into activity recognition models with the helpof machine learning algorithms. As a delimitation, activity recognition was limited to falldetection. These fall detection models were then hosted on a basic server to test accuracyand to act as an example use case for the rest of the project. The project resulted in an effective data gathering system and was generally successful asa tool to create datasets. The iMotions platform was especially successful in both visualizingand recording data together with the data export application. The example fall detectionmodels trained showed theoretical promise, but failed to deliver good results in practice,partly due to the limitations of the positional sensor system used. Some of the conclusions drawn at the end of the project were that the data collectionprocess needed more structure, planning and input from professionals, that a better positionalsensor system may be required for better fall detection results but also that this kind of systemshows promise in the context of smart homes, especially within areas like elderly healthcare.