Recently, smart home applications are increasing fast, including but not limited to occupancy-dependent control of lighting, heating and cooling. Passive infrared (PIR) sensors play an important role in these applications to perceive the presence and/or the motion of human. However, PIR sensors are not able to detect stationary occupants while stationary presence takes up most time of the day. And thus, the resulted false negative detection leads to uncomfortable light/temperature swings, shortened equipment’s lifespan, and/or energy waste, etc. To address this issue, our group has developed Synchronized Low-Energy Electronically-chopped PIR (SLEEPIR) sensors that integrate an electronic liquid crystal (LC) infrared shutter with an off-the-shelf PIR sensor. In this work, the infrared shutter made of polymer dispersed liquid crystal (PDLC) sandwiched by two germanium windows is proposed to periodically chop the long-wave infrared signal received by the PIR sensor so that stationary human presence can still be detected due to the electronical shuttering. A sensor module is fabricated, consisting of a wireless microcontroller, a SLEEPIR sensor and a traditional PIR sensor, with a field of view of 103° × 103°. Then, a sensor network consists of two sensor modules is developed. Three types of experiments are conducted in this paper: individual action-based, continuous activity-based, and daily routine-based. The detection logic is made by using the threshold value classification method, where the threshold values are determined from the action-based dataset and applied to the other two datasets. The results show that for activity-based dataset, the average accuracy reached 98.96%. For daily routine-based dataset, the average accuracy is 99.57%.