Earticle

Infrared scanning systems have emerged as an effective solution for detecting physical contact with surfaces. These systems typically consist of infrared transmitters and receivers installed on opposite edges of a plane, aligned to face each other. Conventionally, such systems utilized a one-to-one configuration—each transmitter paired with a specific receiver—enabling quick response and straightforward system design. However, this setup suffers from a key drawback: the inability to detect multiple contact points at the same time. To overcome this limitation, the one-to-many scanning approach has been introduced. In this method, a single transmitter sends out infrared signals that are simultaneously received by multiple detectors on the opposing side. This allows the system to analyze real-time signal disruptions to estimate both the position and size of contacting objects. Recent advances in computational power have significantly enhanced the speed and accuracy of this method. Nevertheless, its application has largely remained confined to flat surfaces, and there has been a lack of theoretical tools for evaluating performance across diverse sensor arrangements and surface geometries. In response to this gap, our study presents a mathematical model of an infrared sensor array system, enabling predictive analysis of performance across various sensor placements on both planar and curved surfaces. Furthermore, we investigate how positional deviations, including misalignments in sensor orientation, impact detection accuracy, which offers a robust framework for quantitatively predicting system performance and will serve as a valuable tool for designing customized sensor configurations for a wide range of future applications.

Infrared-based scanners are utilized as a promising method for detecting objects that contact on a surface. In this system, infrared transmitters and receivers are positioned at opposite ends of the plane, facing each other. Traditionally, this system employed a one-to-one scanning method, where a single infrared transmitter emits a light signal that is detected by a corresponding receiver on the opposite side. While this method offers advantages such as fast response times and system simplicity, it is limited by its inability to detect multiple objects simultaneously. To address this limitation, recent applications have adopted the one-to-many scanning. In this scanning method, a single infrared transmitter emits a light signal that is detected by multiple receivers on the opposite side. The results are then read in real-time to determine the position and size of the object. With the recent advancements in computing power, the response speed and accuracy of one-to-many scanning have significantly improved. However, in most cases, this method has been limited to object detection on simple planes, and there is no analytical method available to support performance prediction when considering various sensor installation configurations with various form-factors. In this study, we mathematically modeled an infrared sensor array system to predict the performance of various sensor configurations installed on twodimensional planes or curved surfaces. Additionally, we assess the critical effect of inevitable positional errors (including orientation mismatches) on the system's performance. The unique approach introduced in this paper will provide highly reliable quantitative predictions, aiding in the design of sensor network form factors tailored for various applications in the future.