Gensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and Reasoning

DOI

Multimodal large language models (MLLMs), with their expansive world knowledge and reasoning capabilities, present a unique opportunity for end-users to create personalized AI sensors capable of reasoning about complex situations. A user could describe a desired sensing task in natural language (e.g., "let me know if my toddler is getting into mischief in the living room"), with the MLLM analyzing the camera feed and responding within just seconds. In a formative study, we found that users saw substantial value in defining their own sensors, yet struggled to articulate their unique personal requirements to the model and debug the sensors through prompting alone. To address these challenges, we developed Gensors, a system that empowers users to define customized sensors supported by the reasoning capabilities of MLLMs. Gensors 1) assists users in eliciting requirements through both automatically-generated and manually created sensor criteria, 2) facilitates debugging by allowing users to isolate and test individual criteria in parallel, 3) suggests additional criteria based on user-provided images, and 4) proposes test cases to help users "stress test" sensors on potentially unforeseen scenarios. In a 12-participant user study, users reported significantly greater sense of control, understanding, and ease of communication when defining sensors using Gensors. Beyond addressing model limitations, Gensors supported users in debugging, eliciting requirements, and expressing unique personal requirements to the sensor through criteria-based reasoning; it also helped uncover users' own "blind spots" by exposing overlooked criteria and revealing unanticipated failure modes. Finally, we describe insights into how unique characteristics of MLLMs–such as hallucinations and inconsistent responses–can impact the sensor-creation process. Together, these findings contribute to the design of future MLLM-powered sensing systems that are intuitive and customizable by everyday users.