Gait is a routine activity performed daily by every able-bodied individual. Recent studies have demonstrated its potential as a reliable factor in behavioral biometrics for user recognition, offering high accuracy. This study presents a novel approach to human gait analysis leveraging spectrogram representations of accelerometer signals combined with deep learning techniques. In proposed methods time-series accelerometer data is transformed into spectrograms using the Short-Time Fourier Transform (STFT). This conversion enables the utilization of convolutional neural networks (CNNs), which are highly effective in image classification tasks, to identify distinctive gait patterns. The architecture of the neural network was carefully optimized through a series of empirical experiments, adjusting the number and order of layers to achieve the best classification performance. The effectiveness of the proposed solution was validated on both a custom dataset and a publicly available benchmark dataset. The proposed CNN-based approach achieved a prediction accuracy of 92.03%, outperforming traditional models based on raw sensor data and demonstrating competitive performance relative to state-of-the-art methods, such as ResNet-based CNNs, GRU networks, and the M-GaitFormer architecture. These results confirm the advantages of time-frequency feature extraction for gait recognition. This work demonstrates the potential of using everyday mobile devices equipped with accelerometers for biometric authentication based on behavioral traits. Future research directions include extending the model to multi-sensor fusion (e.g., incorporating gyroscope data), enhancing robustness across diverse walking conditions, and developing efficient implementations for real-time gait recognition on mobile platforms.

The article presents a new method of multibiometric Verification that has been enhanced with dynamic classifier selection based on determining their competence. The competence of a classifier is defined, taking into account the type of biometric trait and the samples analyzed. The proposed approach not only allows for adaptive selection of classifiers to specific features and samples but also increases system efficiency by more effectively utilizing models that best match the given conditions. Classifier selection occurs dynamically, which enables the system to adjust flexibly to changing conditions and leverage the strengths of individual models while mitigating their weaknesses through compensation within the committee. The effectiveness of this method has been verified through experiments, which confirmed a significant improvement in person Verification effectiveness compared to methods without dynamic classifier selection.

Riskful and aggressive driving are significant problems in road transportation. Numerous studies prove drivers’ behavior is critical in most road accidents and contributes significantly to fuel consumption and emissions. In an attempt to improve road safety, it is important to be able to detect such unskilful and irresponsible drivers on a mass scale. Thus an automated onboard driving safety assessment system is required. Transmitting amounts of telemetric data required to assess driving style is unpractical and economically unjustified, thus some data processing must take place in an Onboard Unit (OBU). As a first step in driving safety assessment is detecting maneuvers, this is the task, that can be done in the OBU. In this paper, we propose a Finite State Machine based algorithm, which could run on OBU and allows marking of basic maneuvers in the telemetry datastream. This allows the calculation of additional features, aggregation, and transmission of data useful for classification the of driving safety, and thus a vast reduction in data volume sent over the air. Also, an example of detecting unsafe maneuver using such aggregated data is given.

This paper proposes a novel method for diagnosing Parkinson's disease, based on a hand-drawn spirals and features generated from them. Analyzed spirals were drawn on a drawing tablet by both ill and healthy subjects. During drawing, coordinates of points of the spiral, pressure and angle of the pen at that point, and timestamp were registered. On the basis of the registered data, a set of features has been proposed, by means of which the classification was performed. For classification, several of the most popular machine learning methods were used, for which the accuracy of Parkinson's disease recognition was determined. The study showed that the proposed set of features enables the effective diagnosis of Parkinson's disease. The experiments were conducted on a publicly available database from the UCI archives. This database contains drawings of spirals made by people with Parkinson's disease and healthy people.

This paper describes a novel method for classification based on a partly incomplete and low quality image data. The method has been tested on images with low quality areas such as partially corrupted, dark, or blurry images. Image data are converted to classifier input. As a result, we are dealing with missing data describing these areas, but the classifier was designed to work in the presence of missing data. The analysis of the literature shows that missing data have a negative impact on the operation of the learning algorithms. This field is still insufficient explored. In our method, we integrate complex image processing techniques, machine learning, and statistical methods under various contexts. The novelty of the presented approach is the structure of the classifier, which works in the presence of missing data on different principles than those used so far. Evaluation was performed on the basis of realistic images, collected before experiments. Multi-variant experimental protocols, specially designed in this work, confirmed the accuracy of the method. The obtained results help to better understand the methods of recognizing objects with incomplete data structures. Analysis techniques proposed in this paper are rapidly gaining in importance, so the proposed approach can be useful in many areas where the machine learning is employed.

The paper proposes a novel signature verification concept. This new approach uses appropriate similarity coefficients to evaluate the associations between the signature features. This association, called the new composed feature, enables the calculation of a new form of similarity between objects. The most important advantage of the proposed solution is case-by-case matching of similarity coefficients to a signature features, which can be utilized to assess whether a given signature is genuine or forged. The procedure, as described, has been repeated for each person presented in a signatures database. In the verification stage, a two-class classifier recognizes genuine and forged signatures. In this paper, a broad range of classifiers are evaluated. These classifiers all operate on features observed and computed during the data preparation stage. The set of signature composed features of a given person can be reduced what decrease verification error. Such a phenomenon does not occur for the raw features. The approach proposed was tested in a practical environment, with handwritten signatures used as the objects to be compared. The high level of signature recognition obtained confirms that the proposed methodology is efficient and that it can be adapted to accommodate as yet unknown features. The approach proposed can be incorporated into biometric systems.