Synthetic Generation of Passive Infrared Motion Sensor Data Using a Game Engine

Stefansson, Petter; Karlsson, Fredrik; Persson, Magnus P. S.

doi:10.3390/s21238078

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, occupancy detection systems with associated cost, privacy concerns, accuracy measurement and quantitative analysis have been discussed. Data from different indoor and outdoor sensors including energy consumption, CO 2 , air temperature, Particulate Matter 2.5 (PM 2.5), illuminance, humidity, Passive Infrared (PIR), smart cameras, etc., are commonly used for occupancy prediction [17]. A system has been presented as an implementation of low-cost sensors for occupancy detection for each office separately in [18].…”

Section: Background and Motivationmentioning

confidence: 99%

A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Types

Rafiq,

Mahmood,

Ahmed

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The occupancy datasets are useful for planning important buildings' related tasks such as optimal design, space utilization, energy management, maintenance, etc. Researchers are currently working on two key issues in building management systems. First, feasible and economical deployment of indoor and outdoor weather and energy monitoring sensors for data acquisition. Second, the development and implementation of cost-effective data-driven models with regular monitoring to ensure satisfactory performance for occupancy prediction. In this context, we present an occupancy forecasting model for different types of rooms in an academic building. A comprehensive dataset comprising indoor and outdoor environmental variables such as energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) operational details and information on Wi-Fi-connected devices of a campus building, is used for occupants' count prediction. A Light Gradient Boost Machine (LGBM) is applied for the selection of suitable features. After the feature selection, Machine Learning (ML) models such as Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), Long Short-Term Memory (LSTM) and Categorical Boosting (CatBoost) are employed to predict occupants' count in each room. The models' performances are evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and Normalized Root Mean Square Error (NRMSE). The proposed LGBM-XgBoost model outperforms other approaches for each type of space. Moreover, to highlight the importance of LGBM as a feature selection technique, the XgBoost model is also trained with all features. Results indicate that by selecting the appropriate features through LGBM, the RMSE and MAE for lecture rooms 1 and 2 are improved by 61.67%, 36.17% and 67.05%, 63.67%, respectively. Similarly, for office rooms 1 and 2 RMSE and MAE are improved by 33.37%, 71.5% and 59.7%, 51.45%, respectively.

show abstract

Section: Background and Motivationmentioning

confidence: 99%

A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Types

Rafiq,

Mahmood,

Ahmed

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Figure 6: PIR SensorThe PIR (Passive Infrared) sensor detects the presence of infrared light emission. PIR sensors are passive, indicating that they do not emit infrared light but instead receive it from the environment[12].…”

mentioning

confidence: 99%

IoT Based Smart Aquarium using NodeMCU

Arifin Noor Asyikin,

Ida Hastuti,

Ahmad Yusuf

et al. 2024

IIAI Letters on Informatics and Interdisciplinary Research

View full text Add to dashboard Cite

Important aspects of aquarium fish maintenance include the timing of fish nutrition, the availability of water, the temperature of the water, and the monitoring of the aquarium's condition. This study aims to develop an IoT-based aquarium, also known as an internet of things aquarium, that is capable of automatically sending images via telegraph, feeding fish, setting the time that the light turns on, detecting the temperature and level of the aquarium water, and setting the time that the light turns off. A Smart Aquarium consists of an Arduino IDE, an ESP8266 Microcontroller, an ESP32 Cam, a Water Level Sensor, a Buzzer, a PIR Sensor, an RTC DS3231 Module, CP1202 Modules USB to TTL, a 12 Volt DC Fan, a Relay, a Lamp, an Adapter, an LCD, and a Motor Servo. Based on functional and unit testing, the functionality of this tool is satisfactory.

show abstract

Synthetic Generation of Passive Infrared Motion Sensor Data Using a Game Engine

Cited by 2 publications

References 18 publications

A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Types

A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Types

IoT Based Smart Aquarium using NodeMCU

Contact Info

Product

Resources

About