Smart environment sensing provides valuable contextual information by detecting occurrences of events such as human activities and changes of object status, enabling computers to collect personal and environmental informatics to perform timely responses to user’s needs. Conventional approaches either rely on tags that require batteries and frequent maintenance, or have limited detection capabilities bounded by only a few coarsely predefined activities. In response, this paper explores corner reflector mechanisms that encode user interactions with everyday objects into structured responses to millimeter wave radar, which has the potential for integration into smart environment entities such as speakers, light bulbs, thermostats, and autonomous vehicles. We presented the design space of 3D printed reflectors and gear mechanisms, which are low-cost, durable, battery-free, and can retrofit to a wide array of objects. These mechanisms convert the kinetic energy from user interactions into rotational motions of corner reflectors which we computationally designed with a genetic algorithm. We built an end-to-end radar detection pipeline to recognize fine-grained activity information such as state, direction, rate, count, and usage based on the characteristics of radar responses. We conducted studies for multiple instrumented objects in both indoor and outdoor environments, with promising results demonstrating the feasibility of the proposed approach.