Room Occupancy Estimation

The experimental testbed for occupancy estimation was deployed in a 6m x 4.6m room. The setup consisted of 7 sensor nodes and one edge node in a star configuration with the sensor nodes transmitting data to the edge every 30s using wireless transceivers. No HVAC systems were used while the dataset was being collected.

Five different types of non-intrusive sensors were used in this experiment: temperature, light, sound, CO2, and digital passive infrared (PIR). The CO2, sound, and PIR sensors needed manual calibration. For the CO2 sensor, zero-point calibration was manually done before its first use by keeping it in a clean environment for over 20 minutes and then pulling the calibration pin (HD pin) low for over 7s. The sound sensor is essentially a microphone with a variable-gain analog amplifier attached to it. Therefore, the output of this sensor is analog which is read by the microcontroller’s ADC in volts. The potentiometer tied to the gain of the amplifier was adjusted to ensure the highest sensitivity. The PIR sensor has two trim pots: one to tweak the sensitivity and the other to tweak the time for which the output stays high after detecting motion. Both of these were adjusted to the highest values. Sensor nodes S1-S4 consisted of temperature, light, and sound sensors, S5 had a CO2 sensor, and S6 and S7 had one PIR sensor each that were deployed on the ceiling ledges at an angle that maximized the sensor’s field of view for motion detection.

The data was collected for a period of 4 days in a controlled manner with the occupancy in the room varying between 0 and 3 people. The ground truth of the occupancy count in the room was noted manually.

authors:Adarsh Pal Singh, Vivek Jain, Sachin Chaudhari, Frank Alexander Kraemer, Stefan Werner and Vishal Garg, "Machine Learning-Based Occupancy Estimation Using Multivariate Sensor Nodes," in 2018 IEEE Globecom Workshops (GC Wkshps), 2018.

该实验测试平台用于占用估计，部署于一个6米乘以4.6米的房间内。该设置包括7个传感器节点和一个边缘节点，采用星形配置，传感器节点每30秒通过无线收发器向边缘节点传输数据。在收集数据集期间，未使用任何暖通空调系统。实验中采用了五种不同类型的非侵入式传感器：温度、光照、声音、二氧化碳以及数字被动红外（PIR）传感器。二氧化碳、声音和PIR传感器需要手动校准。对于二氧化碳传感器，在首次使用前，通过将其放置在清洁环境中超过20分钟，然后拉低校准针（HD针）超过7秒，进行零点校准。声音传感器实际上是一个带有可变增益模拟放大器的麦克风，因此其输出为模拟信号，由微控制器的模数转换器（ADC）以伏特为单位读取。连接到放大器增益的电位器被调整以确保最高的灵敏度。PIR传感器具有两个微调电位器：一个用于调整灵敏度，另一个用于调整检测到运动后输出保持高电平的时间。这两个电位器均调整至最大值。传感器节点S1-S4包含温度、光照和声音传感器，S5配备二氧化碳传感器，而S6和S7各配备一个PIR传感器，这些传感器被部署在天花板边缘，以最大化传感器的运动检测视野。数据以受控方式收集了4天，房间内的占用人数在0至3人之间变化。房间内占用人数的真实值是手动记录的。