PDC Sensor for Person Detection - Ultrasonic Sensor Networks and Machine Learning for Indoor Occupancy Monitoring and Activity Recognition
This technical article explores the ultrasonic sensor network architecture and machine learning techniques for indoor person detection and activity recognition, covering the sensor placement strategies, the data pre-processing and feature extraction methods, the classification algorithms for activity discrimination, and the system integration for smart building automation.
The ultrasonic sensor network for person detection consists of multiple sensors strategically positioned to provide comprehensive coverage of the monitored indoor environment. The sensors are typically mounted on ceilings or walls, with their beams directed at the areas of interest. The sensor placement is optimized to minimize blind spots while maximizing coverage, with the beam angles and ranges selected based on the room geometry and the expected activities. The sensors are connected to a central processing unit via wired or wireless communication, with each sensor transmitting distance measurements at a specified sampling rate. The sensor network can be scaled to cover large areas by adding additional sensors, with the system architecture supporting distributed processing for real-time operation. The use of non-invasive and non-wearable sensors ensures that the monitoring does not interfere with the occupants' activities, making it suitable for office environments, healthcare facilities, and smart homes.
PDC Sensor
The data pre-processing and feature extraction methods transform raw ultrasonic measurements into meaningful inputs for classification algorithms. The raw data undergoes calibration to filter out environmental noise, followed by segmentation into time windows (typically 5 seconds). The root mean square temporal marker is extracted from each window, providing a measure of the signal variation over time. Additional features, such as the signal amplitude and the rate of change of distance, are also extracted to capture the dynamic characteristics of human activities. The pre-processed data is then used as input for the classification models. The segmentation of the studied area and training of the model to specific regions improves the accuracy of position detection. The radial and axial position of the detected person relative to the measuring system can be determined, enabling precise localization.
The classification algorithms for activity discrimination employ both traditional machine learning and deep learning techniques. The two-stage classification approach first distinguishes between static and dynamic activities using a support vector machine (SVM) classifier, achieving 93.1% accuracy. The second stage uses a convolutional neural network (CNN) to classify specific dynamic activities, such as writing, typing, talking on the phone, and standing, with a mean accuracy of 99.3%. The CNN is trained on labeled datasets of ultrasonic sensor data from participants performing predefined activities. The high accuracy of the classification enables reliable activity recognition for applications such as personalized comfort models and resource usage optimization. The use of deep learning also enables the system to adapt to new activity patterns over time, improving its long-term performance.
The system integration for smart building automation uses the person detection and activity recognition data to optimize building operations. The occupancy information is used to control lighting, HVAC, and other building systems, reducing energy consumption when spaces are unoccupied. The activity recognition data provides insights into how spaces are used, enabling facility managers to optimize space utilization and improve occupant comfort. The system can also be integrated with security systems to detect unauthorized presence or unusual activity patterns. In healthcare settings, the activity recognition can be used to monitor the well-being of elderly or disabled individuals, detecting falls or changes in activity patterns that may indicate health issues. The system's ability to detect still persons using supervised learning over segmented reflection patterns ensures that occupants who are sitting or standing still are still detected, providing comprehensive occupancy monitoring.
The ongoing development in ultrasonic person detection is focused on improving the accuracy and robustness of the classification algorithms. The use of more advanced deep learning architectures, such as temporal convolutional networks and transformers, is being explored to capture the temporal dynamics of human activities more effectively. The integration of ultrasonic sensors with other sensing modalities, such as passive infrared (PIR) and acoustic sensors, is improving the reliability of person detection in challenging conditions. The development of low-power, wireless ultrasonic sensor nodes is enabling the deployment of large-scale sensor networks for comprehensive building monitoring. The ultrasonic person detection system continues to evolve, providing the reliable, non-invasive sensing required for smart building automation, security, and healthcare applications.
