A novel event detector algorithm, which points out in-door acoustic human activities, for constrained wireless sensor node hardware is proposed in the present paper. In our approach, event detections are computed from the signal energy statistics change rate at two instants separated by an ( L − 1 ) samples interval. The experimentation is run in two phases: (i) the detector characterisation and tuning seek detector configurations that enable event detections from three acoustic human activities: closing a door, dropping a plastic bottle, and clapping; (ii) event detector validation tests measure the reliability to signal events from general acoustic activities, people talking particularly. The test results, which included emulated node hardware, actual sensor node, and a one-hop WSN, demonstrate the detector implementations signaled successfully events. And for the WSN, we found that event detections decay in a nonlinear fashion as the distance d , between the acoustic signal source and the sensor, is increased.