In this paper, we present a mobile crowdsensing approach to understand the daily lifestyle of the older population in Singapore. By implementing novel clustering, sensor fusion, and user profiling techniques to analyze the multisensor data (location, noise, and light) collected from a smartphone application, we identified the travel patterns at several points of interest (POI), the impact of travel frequency for certain POI, and three main user profiles. The results show that older adults mostly spend time at food courts and community centers in their home neighborhood, but they travel away from the neighborhood for healthcare and religious purposes. We found that POIs have more visits if they are easily accessible (in terms of travel time from home) regardless of the distance from home.