In this paper a sensor system for traffic data acquisition is presented. The embedded system, comprising a motion-sensitive optical sensor and a low-cost, low-power DSP, is capable of detecting, counting and measuring the velocity of passing vehicles. The detection is based on monitoring of the optical sensor output within configurable regions of interest in the sensor's field-of-view. In particular in this work we focus on the evaluation of the applied vehicle counting algorithm. The verification of the acquired data is based on manually annotated traffic data of 360 minutes length, containing a total of about 7000 vehicles. The counting error is determined for short (3 minutes) and long (60 minutes) time intervals. The calculated error of 99,2% of the short time intervals and 100% of the long time intervals analyzed, remain within commonly recognized margins of 10% and 3% of detection error respectively.

Objective: The purpose of this study was to derive a respiratory movement signal from a 3D time-of-flight camera and to investigate if it can be used in combination with SpO $_2$ to detect respiratory events comparable to polysomnography (PSG) based detection. Methods: We derived a respiratory signal from a 3D camera and developed a new algorithm that detects reduced respiratory movement and SpO $_2$ desaturation to score respiratory events. The method was tested on 61 patients' synchronized 3D video and PSG recordings. The predicted apnea-hypopnea index (AHI), calculated based on total sleep time, and predicted severity were compared to manual PSG annotations (manualPSG). Predicted AHI evaluation, measured by intraclass correlation (ICC), and severity classification were performed. Furthermore, the results were evaluated by 30-second epoch analysis, labelled either as respiratory event or normal breathing, wherein the accuracy, sensitivity, specificity and Cohen's kappa were calculated. Results: The predicted AHI scored an ICC $r$ = 0.94 (0.90 — 0.96 at 95% confidence interval, $p$ < 0.001) compared to manualPSG. Severity classification scored 80% accuracy, with no misclassification by more than one severity level. Based on 30-second epoch analysis, the method scored a Cohen's kappa= 0.72, accuracy = 0.88, sensitivity = 0.80, and specificity = 0.91. Conclusion: Our detection method using SpO $_2$ and 3D camera had excellent reliability and substantial agreement with PSG-based scoring. Significance: This method showed the potential to reliably detect respiratory events without airflow and respiratory belt sensors, sensors that can be uncomfortable to patients and susceptible to movement artefacts.

Analyses of sleep-related movement disorders have gained importance due to an increase in life expectancy. The present approaches for measuring movements are based on electromyography or accelerometry and provide only local or specific results from muscles/limbs to which sensors have been attached. The motivation of this work was to investigate the detection of a more complete spectrum of sleep-related movements using a three-dimensional (3D) camera instead of the current conventional methods. In contrast to most of the previously published literature, this method allows for the detection of movements even when patients are covered with a blanket. This is the first work to evaluate movement detection with a clinical dataset and replicate the clinical environment in a laboratory setup. The laboratory setup allowed for the characterization of detectable movements through the determination of speed and amplitude limits. We used the Kinect One time-of-flight sensor to record 3D videos. Movements were quantified based on the temporal depth change in these 3D videos. A computer-controlled lifting table allowed for the controlled simulation of movements. Our algorithm detected movements with amplitude values >3.0 mm and velocity values >3.5 mm/s with an F1 score ≥95%. The shortest reliably detected duration of movement was 350 ms. In an ethically approved clinical study including 44 patients, 93.1% of electromyography-detected leg movements were also found in 3D. A significant correlation ($\rho = 0.86$ ) was found between movements detected by the 3D system and polysomnography. The 3D system detected 31.2% more movements than electromyography. In addition to obtaining a broader spectrum of movements not limited to local and muscle/limb-specific movements, the usage of a contactless 3D camera simplifies the recording setup and preserves natural sleeping behavior. The presented 3D system may become useful for diagnostic purposes during sleep studies.

Contactless fingerprint recognition systems offer a hygienic, user-friendly, and efficient alternative to traditional contact-based methods. However, their accuracy heavily relies on precise fingertip detection and segmentation, particularly under challenging background conditions. This paper introduces TipSegNet, a novel deep learning model that achieves state-of-the-art performance in segmenting fingertips directly from grayscale hand images. TipSegNet leverages a ResNeXt-101 backbone for robust feature extraction, combined with a Feature Pyramid Network (FPN) for multi-scale representation, enabling accurate segmentation across varying finger poses and image qualities. Furthermore, we employ an extensive data augmentation strategy to enhance the model’s generalizability and robustness. This model was trained and evaluated using a combined dataset of 2257 labeled hand images. TipSegNet outperforms existing methods, achieving a mean intersection over union (mIoU) of 0.987 and an accuracy of 0.999, representing a significant advancement in contactless fingerprint segmentation. This enhanced accuracy has the potential to substantially improve the reliability and effectiveness of contactless biometric systems in real-world applications.

Biometric identification systems, particularly those utilizing fingerprints, have become essential as a means of authenticating users due to their reliability and uniqueness. The recent shift towards contactless fingerprint sensors requires precise fingertip segmentation with changing backgrounds, to maintain high accuracy. This study introduces a novel deep learning model combining ResNeSt and UNet++ architectures called FingerUNeSt++, aimed at improving segmentation accuracy and inference speed for contactless fingerprint images. Our model significantly outperforms traditional and state‐of‐the‐art methods, achieving superior performance metrics. Extensive data augmentation and an optimized model architecture contribute to its robustness and efficiency. This advancement holds promise for enhancing the effectiveness of contactless biometric systems in diverse real‐world applications.

Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms’ fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.