Improving Indoor Occupancy Detection Accuracy of the SLEEPIR Sensor Using LSTM Models

We recently developed a synchronized low-energy electronically chopped passive infrared (SLEEPIR) sensor node to detect stationary and moving occupants. It uses a liquid crystal shutter to modulate the infrared signal received by a traditional passive infrared (PIR) sensor and thus enables its capability to detect stationary occupants. However, the detection accuracy of the SLEEPIR sensor can be easily influenced by infrared environmental disturbances. To address this problem, in this article, we propose two long short-term memory (LSTM) models to filter infrared environmental disturbance, named baseline LSTM (Base.LSTM) and statistical LSTM (Stat.LSTM). They use the sensor node raw output and statistical features as their respective input. For comparison, we propose two other models: the occupancy state switch detection (SSD) algorithm that directly uses a predetermined threshold voltage value to classify the occupancy state and its status change; and the multilayer perception (MLP) classifier with statistical feature inputs (Stat.ML). To validate their detection performance, we designed two testing scenarios in different environment settings: 1) daily occupancy tests and 2) EDGE case tests. The first scenario intends to restore complex real-life environmental situations as much as possible in the lab and apartment rooms. The second scenario aims to verify their detection accuracy under different environmental temperatures. This scenario also considers different occupancy postures, such as lying down. Experimental results show that the detection accuracy of both LSTM models ( $>$ 95%) in both testing scenarios outperforms that of the SSD (around 82%–94%) and the Stat.ML (around 80%–90%).