Smart home sensor data is being increasingly used to identify health risks through passive tracking of specific behaviours and activity patterns. This study explored the feasibility of using motion sensor data to track changes in daytime movement patterns within the home, and their potential association with depression in older adults. This study analysed the motion sensor data collected during a one-year smart home trial, and explored their association with Geriatric Depression Scale (GDS) scores collected at three different time points during the trial (i.e., baseline, mid-trial, and end-trial). Our results showed that movement patterns are generally reduced when older adults are in a depressed state compared to when being in a not-depressed state. In particular, the reduced movement activity in depressed states was significant (p<.05) when the participant's GDS state changed between depressed and not-depressed for the first time during the three time points of the trial when GDS was collected.Clinical relevance- Our results establish the feasibility and potential use of motion sensor data from ambient sensors in a smart home for passive and remote assessment of older adults' depression status, that is comparable to their GDS scores, through changes in their in-home day-time movement patterns. Also since reduced movement activity may be a general indicator of potential health risks, this study provides preliminary evidence for using in-home movement activity monitoring as an general indicator of health risks.