The demand for efficient human activity recognition systems has surged recently, driven by the need for intelligent monitoring in various environments such as smart homes and workplaces. This paper presents a novel approach to measuring human activeness using a single Passive Infrared (PIR) sensor, highlighting its simplicity, cost-effectiveness, and privacy-conscious design. This paper introduces a novel one-dimensional modeling approach for measuring human activeness using a single Passive Infrared (PIR) sensor, incorporating the Laplace distribution to analyze movement patterns. We define an activeness index \(\mu \) , quantifying average human activity over time, allowing for precise numerical assessment. Our method utilizes the sensor’s capabilities to gather data on human movement and generate numerical metrics of average activeness over time. The results demonstrate that this approach effectively captures human activity levels while minimizing equipment complexity. This work contributes to the growing field of human activity recognition by offering a practical solution that balances performance with user privacy and affordability.