<p dir="ltr">Recognition of human behavior and object status plays a significant role in context-aware applications. Although researchers have explored methods to detect users’ activities, there still exists a research gap where end-users are not able to build personalized applications that accurately recognize their activities in the physical environment. Therefore, in this thesis, to fill the gap, we explore different in-situ context-aware Augmented Reality (AR) applications that leverage hand-object recognition and support users to rapidly author context-aware applications by referring to their activities. We first explore the possibility that end-users develop AR applications with customized freehand interactions and introduce GesturAR, an end-to-end authoring tool that supports users to create freehand AR applications through embodied demonstration and visual programming. Next, we explore hand interactions with physical objects and propose ARnnotate, an AR system enabling end-users to create custom data. To further study context-aware applications with haptic feedback, we present Ubi Edge, an AR authoring tool that allows end-users to customize edges on daily objects as tangible user interface (TUI) inputs to control varied digital functions. We develop an integrated AR device and a vision-based detection workflow to track 3D edges and detect the touch interaction between fingers and edges. Finally, to enable end-users to apply AR applications in different physical environments, we propose AdapTUI, a system that can automatically adapt the geometric-based TUIs in various environments. In summary, our contribution lies in the development of several context-aware AR applications that effectively sense users’ activities and facilitate users’ authoring process intuitively and conveniently.</p>