Mateusz Daniol

This article presents a method for calibration of dynamic vehicle weighing systems (WIM—Weigh-In-Motion) involving the calibration of all WIM stations operating within a given road network segment as a single process. A key assumption of the method is the presence of at least one scale with significantly higher accuracy than the calibrated systems in this part of road network. This reference scale function may be played by a static scale, slow-pass scale (LS-WIM—Low-Speed WIM) for measurement of vehicle axle load or by a selected WIM system with heightened accuracy. Both the reference scale and all systems undergoing calibration must be equipped with a system for the automatic recognition of vehicle registration number plates. The reference scale makes it possible to determine axle load values considered as benchmark values. Then, for each vehicle weighed on the reference scale and subsequently on any WIM system operating within the analysed area, the relative difference between the reference result and the WIM system measurement is calculated with respect to the reference value. This difference forms the basis for the operation of the algorithm estimating the coefficients of the static characteristic of the calibrated WIM system (so-called calibration coefficients), which are then used to determine corrected weighing results. The estimation of the coefficients is updated after each identified vehicle that has previously been weighed on the reference scale is considered. The article presents both the results of simulations and experimental studies concerning the proposed spatial method of calibration. The results obtained allow for an assessment of the effectiveness of the proposed solution. As can be seen from the analyses conducted, this method leads to a significant reduction in systematic error of vehicle weight measurement. Unfortunately, it does not eliminate random errors. The spatial calibration approach described in this paper has certain limitations. The main ones include the impact of ANPR system errors on calibration effectiveness, cases where a vehicle is unloaded or loaded between WIM stations, and the propagation of systematic errors from the reference systems to the other WIM systems. A significant advantage of the proposed spatial calibration method is that it can operate effectively using weighing data from a single reference WIM system and does not require heavy traffic volumes.

Parkinson’s disease (PD) alters both speech and movement, yet most automated assessments still treat these signals separately. We examined whether combining voice with head motion improves discrimination between patients and healthy controls (HC). Synchronous measurements of acoustic and inertial signals were collected using a HoloLens 2 headset. Data were obtained from 165 participants (72 PD/93 HC), following a standardized mixed-reality (MR) protocol. We benchmarked single-modality models against fusion strategies under 5-fold stratified cross-validation. Voice alone was robust (pooled AUC ≈ 0.865), while the inertial channel alone was near chance (AUC ≈ 0.497). Fusion provided a modest but repeatable improvement: gated early-fusion achieved the highest AUC (≈0.875), cross-attention fusion was comparable (≈0.873). Gains were task-dependent. While speech-dominated tasks were already well captured by audio, tasks that embed movement benefited from complementary inertial data. Proposed MR capture proved feasible within a single session and showed that motion acts as a conditional improvement factor rather than a sole predictor. The results outline a practical path to multimodal screening and monitoring for PD, preserving the reliability of acoustic biomarkers while integrating kinematic features when they matter.

This study introduces an innovative multimodal system designed to support the assessment of Parkinson’s disease, atypical Parkinsonian syndromes, and Huntington’s disease by integrating mixed-reality (MR) technology, specifically using augmented reality goggles. The system captures symptoms related to neurodegenerative disorders through detailed sensor data, including hand movements, gait, and gaze patterns. Central to our methodology is an interactive, game-like experience lasting 30 min, comprising 17 tasks that evaluate motor skills, speech, memory, cognitive functions, gait, and gaze. These tasks were developed in collaboration with neurologists and draw upon established clinical paradigms. This engaging experience, combined with traditional clinical tests, provides a comprehensive assessment of both physical and cognitive capabilities. Rather than aiming to provide a diagnostic decision, this study focuses on evaluating the feasibility and usability of the system in a clinical setting. By automating diagnostic tasks and utilizing MR technology, our approach enhances patient comfort and offers a more immersive and efficient assessment process. The system was evaluated with 15 neurodegenerative patients and 32 healthy subjects, demonstrating high scores in both immersion and usability across both groups. This work represents a first step toward integrating MR into clinical workflows for neurodegenerative disease assessment.

The noticeable growth of road transport means that the protection of road infrastructure is becoming a critical issue. The main factor leading to the excessive degradation of roads are overloaded vehicles. The effective elimination of such vehicles from road traffic is possible through widespread usage of Weigh-In-Motion (WIM) systems for direct mass enforcement, thus eliminating the need for “manual” vehicle checks which are currently carried out by the appropriate services. WIM mass enforcement systems require strict metrological control, meaning that an initial verification, conducted at the moment when the system is installed, and subsequent periodic verifications are required. These operations aim to ensure that vehicle weighing error is consistently maintained within a permissible range of values. Fulfilment of this condition allows for the minimisation of the probability that a vehicle loaded within normative limits will be classified as overloaded. The long-term study of two WIM systems located on provincial road 975 in Wielka Wies, in southern Poland, equipped with load sensors made using different technologies (strain gauge sensors and quartz sensors) and in different weather conditions, has allowed us to formulate recommendations regarding the frequency with which subsequent verifications should be performed in order to ensure the reliability of the weighing results. This paper presents the results of these studies and conclusions formulated based on them; in this case, they showed a verification of the system can be performed every 8 months. The conclusions and recommendations that we have presented concern primarily those WIM stations which were the object of our study and caution should be exercised when generalising these to other cases. Its novelty results from several premises. For the first time, long-term studies of two WIM systems equipped with load sensors made with different technologies were carried out. Both systems were installed on the same surface, in the immediate vicinity of each other. They were installed on a standard road and were subjected to the constant impact of road traffic with identical parameters. Tests of both WIM systems were performed periodically, using the pre-weighed vehicles method, in different seasons, for a period of 15 months. During the tests, the same test vehicles drove through both WIM systems at the same speed. All of this resulted in the obtainment of a unique set of measurement data, the analysis of which allowed for the assessment and comparison of the proprieties of the load sensors made with both technologies.

This study explores an innovative approach to early Parkinson’s disease (PD) detection by analyzing speech data collected using a mixed reality (MR) system. A total of 57 Polish participants, including PD patients and healthy controls, performed five speech tasks while using an MR head-mounted display (HMD). Speech data were recorded and analyzed to extract acoustic and linguistic features, which were then evaluated using machine learning models, including logistic regression, support vector machines (SVMs), random forests, AdaBoost, and XGBoost. The XGBoost model achieved the best performance, with an F1-score of 0.90 ± 0.05 in the story-retelling task. Key features such as MFCCs (mel-frequency cepstral coefficients), spectral characteristics, RASTA-filtered auditory spectrum, and local shimmer were identified as significant in detecting PD-related speech alterations. Additionally, state-of-the-art deep learning models (wav2vec2, HuBERT, and WavLM) were fine-tuned for PD detection. HuBERT achieved the highest performance, with an F1-score of 0.94 ± 0.04 in the diadochokinetic task, demonstrating the potential of deep learning to capture complex speech patterns linked to neurodegenerative diseases. This study highlights the effectiveness of combining MR technology for speech data collection with advanced machine learning (ML) and deep learning (DL) techniques, offering a non-invasive and high-precision approach to PD diagnosis. The findings hold promise for broader clinical applications, advancing the diagnostic landscape for neurodegenerative disorders.