Kurze, AlbrechtReuter, ChristinKlein, MaikeKrupka, DanielWinter, CorneliaGergeleit, MartinMartin, Ludger2024-10-212024-10-212024978-3-88579-746-32944-7682https://dl.gi.de/handle/20.500.12116/45193The increasing presence of sensors in smart homes generates vast amounts of data, which require effective interpretation to be useful, often along with data annotation. While automatic approaches can automatically analyze sensor data but require strict and clean annotations, they often neglect the complex, multidimensional nature of human sensemaking. We explore this gap and propose an approach to bridge this gap. We present preliminary findings from three directions: lay user annotations of sensor data collected in a field study using our Sensorkit solution, analysis of existing annotation tools, and a human-centered design process for a new annotation solution. Our goal is to develop a more integrated approach to sensor data interpretation that benefits both humans and machines.enSmart HomeIoTHCISensorsSensor DataSensemakingAnnotationData WorkAIMLArtificial IntelligenceMachine LearningInternet of ThingsHuman-Computer InteractionText/Conference Paper10.18420/inf2024_341617-54682944-7682