Slow design using slow technology in the domain of human–computer interaction is often utilized to reflect people’s attachment to behaviors or objects and to promote positive changes in people’s settling behaviors and the meaning behind them. In this paper, we propose a three-step slow sensor model for developing a slow sensor for classifying the behaviors of the elderly during gardening activities. Step 1: Finding daily objects and a suitable slow activity using the behavior setting. Step 2: System modeling with the corresponding sensors set to collect data. Step 3: Data modeling by designing data schema and behavioral classification. An experiment using a prototype slow sensor, gardening as the slow activity, and IoT with sensor network technology is conducted and data are collected and analyzed to determine the daily incremental activities of the elderly. The results are evaluated and validated for behavioral classification after two weeks of experimentation.