Using Topic Models to Mine Everyday Object Usage Routines Through Connected IoT Sensors
This work addresses the challenge of continuously monitoring object usage for HCI and design research, offering an alternative to sparse ethnographic methods, though it is incremental as it applies existing topic models to new sensor data.
The authors tackled the problem of capturing human resourcefulness in everyday life by deploying IoT sensors on household objects for over two weeks with three elderly participants, using probabilistic topic models to identify common usage patterns.
With the tremendous progress in sensing and IoT infrastructure, it is foreseeable that IoT systems will soon be available for commercial markets, such as in people's homes. In this paper, we present a deployment study using sensors attached to household objects to capture the resourcefulness of three individuals. The concept of resourcefulness highlights the ability of humans to repurpose objects spontaneously for a different use case than was initially intended. It is a crucial element for human health and wellbeing, which is of great interest for various aspects of HCI and design research. Traditionally, resourcefulness is captured through ethnographic practice. Ethnography can only provide sparse and often short duration observations of human experience, often relying on participants being aware of and remembering behaviours or thoughts they need to report on. Our hypothesis is that resourcefulness can also be captured through continuously monitoring objects being used in everyday life. We developed a system that can record object movement continuously and deployed them in homes of three elderly people for over two weeks. We explored the use of probabilistic topic models to analyze the collected data and identify common patterns.