Cristina Floriana Pană

Soft wearable actuators offer compliant, lightweight assistance for rehabilitation and motor support, but their translation beyond controlled laboratories depends on reliable sensing, fault management, and adaptive control. This systematic review maps the evidence across three linked domains: fault detection and diagnosis (FDD), sensing, and intelligent control. Owing to marked heterogeneity in actuator types, validation settings, and reported outcomes, the evidence was synthesised narratively and described using publication counts and proportions rather than pooled performance estimates. The study-level supplementary appraisal covers all 106 publications included in the review. Four normative standards were retained within this total as normative context and were marked as not applicable for empirical methodological appraisal; they were not subtracted from the corpus count. Across the 106 included records, 40 were classified as direct, 57 as adjacent, five as indirect, and four as normative context. The FDD subset was especially limited: nine records provided adjacent evidence and five were indirect under the strict soft-wearable rehabilitation criterion. The review therefore identifies soft-actuator-specific FDD benchmarks, multimodal health-state estimation, and fault-aware control as actionable priorities, while distinguishing these structural gaps from constraints expected in a technologically young field. The completed review was retrospectively registered in the OSF Registries on 21 July 2026 using the Open-Ended Registration template.

Multimodal sensor fusion is increasingly used to improve observability for fault diagnosis and prognostics, enabling Remaining Useful Life estimation in complex mechatronic and robotic systems. Yet, real-world deployments remain vulnerable to sensor faults and data integrity issues—including bias and drift, miscalibration, dropouts, saturation, cross-talk, time desynchronization, and domain shift—which can propagate through fusion pipelines and lead to optimistic validation and poor generalization. These challenges are particularly consequential in safety- and health-adjacent applications such as collaborative robots, wearable/rehabilitation devices, and human-centric mechatronic systems where decisions based on faulty sensing may affect both reliability and user safety. This review synthesizes the state of the art on (i) sensor fault taxonomies and fault models relevant to multimodal fusion, (ii) fault-aware fusion strategies spanning data-, feature-, and decision-level integration, and (iii) how sensor faults and uncertainty impact diagnosis and remaining-life estimators. We will conduct a systematic scoping review of peer-reviewed literature, extracting sensor modalities, fault characterization or injection protocols, fusion architectures, validation settings (simulation, hardware-in-the-loop, bench, and in-field/on-body studies), and reporting completeness. Beyond summarizing methods, we provide practical reporting standards for sensor-fusion-based diagnosis and prognostics, including a minimum disclosure set covering synchronization, fault ground truth, missingness handling, leakage controls, uncertainty calibration, and task-relevant metrics. Reusable checklists and evidence tables are included to support more comparable, reproducible, and deployment-ready research.

Early risk stratification of COVID-19 severity in oncology patients is critical for improving clinical outcomes and optimizing hospital resource allocation. This study proposes a rule-based clinical decision support system (CDSS) designed for integration into digital triage workflows. In practical terms, the score is intended to be applied at hospital admission or triage, where demographic and comorbidity information is routinely available. The computed score can automatically flag high-risk oncology patients for intensified monitoring or early ICU evaluation, supporting rapid resource allocation while preserving clinician decision-making. Using retrospective clinical data from hospitalized oncological patients with confirmed SARS-CoV-2 infection, we developed a scoring algorithm based on four common comorbidities: age ≥ 70, obesity, diabetes mellitus, and hypertension. Each factor was assigned a weighted contribution to a cumulative score ranging from 0 to 7. Patients were classified into three risk levels (low, moderate, high), correlating with observed rates of ICU admission and mortality. The system is built for low-complexity implementation in electronic health records (EHRs) or web-based triage dashboards and includes a software logic model with pseudocode. Results indicate that the score effectively distinguishes patient risk levels with statistical significance (p < 0.01), and can function as an early triage mechanism. The proposed model does not require laboratory data or imaging, making it particularly suitable for rapid deployment in both hospital and remote settings. This work demonstrates a pragmatic, interpretable, and scalable approach to clinical decision support in pandemic contexts involving vulnerable populations such as cancer patients.

This review synthesises fault detection and diagnosis (FDD) methods for robotic actuation in healthcare, where precise, compliant, and safe physical human–robot interaction (pHRI) is essential. Actuator families—harmonic-drive electric transmissions, series-elastic designs, Cable/Bowden mechanisms, permanent-magnet synchronous motors (PMSM), and force–torque-sensed architectures—are mapped to characteristic fault classes and to sensing, residual-generation, and decision pipelines. Four methodological families are examined: model-based observers/parity relations, parameter-estimation strategies, signal-processing with change detection, and data-driven pipelines. Suitability for pHRI is assessed by attention to latency, robustness to movement artefacts, user comfort, and fail-safe behaviour. Aligned with ISO 14971 and the IEC 60601/80601 series, a validation framework is introduced, with reportable metrics—time-to-detect (TTD), minimal detectable fault amplitude (MDFA), and false-alarm rate (FAR)—at clinically relevant thresholds, accompanied by a concise reporting checklist. Across 127 studies (2016–2025), a pronounced technology-dependent structure emerges in the actuator-by-fault relationship; accuracy (ACC/F1) is commonly reported, whereas MDFA, TTD, and FAR are rarely documented. These findings support actuation-aware observers and decision rules and motivate standardised reporting beyond classifier accuracy to enable clinically meaningful, reproducible evaluation in contact-rich pHRI.

Fault detection and diagnosis (FDD) in power plant systems is a rapidly evolving field driven by the increasing complexity of industrial infrastructure and the demand for reliability, safety, and predictive maintenance. This review presents a structured and data-driven synthesis of 185 peer-reviewed articles, sourced from journals indexed in MDPI and Elsevier, as well as through the Google Scholar search engine, published between 2019 and 2025. The study systematically classifies these articles by plant type, sensor technology, algorithm category, and diagnostic pipeline (detection, localization, resolution). The analysis reveals a significant transition from traditional statistical methods to machine learning (ML) and deep learning (DL) models, with over 70% of recent studies employing AI-driven approaches. However, only 30.3% of the articles addressed the full diagnostic pipeline and merely 17.3% targeted system-level faults. Most research remains component-focused and lacks real-world validation or interpretability. A novel taxonomy of diagnostic configurations, mapping system types, sensor use, algorithmic strategy, and functional depth is proposed. In addition, a methodological checklist is introduced to evaluate the completeness and operational readiness of FDD studies. Key findings are summarized in a comparative matrix, highlighting trends, gaps, and inconsistencies across publication sources. This review identifies critical research gaps—including the underuse of hybrid models, lack of benchmark datasets, and limited integration between detection and control layers—and offers concrete recommendations for future research. Combining a thematic and quantitative approach, this article aims to support researchers, engineers, and decision-makers in developing more robust, scalable, and transparent diagnostic systems for power generation infrastructure.

Introduction: This study aims to construct a predictive model that classifies the severity of coronavirus disease 2019 (COVID-19) in cancer patients by analyzing clinical, radiological, and demographic data. The goal is to assist healthcare providers in identifying high-risk patients and ensuring optimal resource allocation. Materials and methods: Data from 237 cancer patients diagnosed with COVID-19 was utilized to forecast disease severity based on various predictors. Key factors included cancer type and stage, intensive care unit admission, radiological assessments, ventilation status, obesity, and systemic inflammatory response syndrome (SIRS). The performance of multiple machine learning (ML) models, including Error-Correcting Output Codes (ECOC) frameworks built on Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Naive Bayes, Discriminant Analysis and an ensemble bagging method, was evaluated using 10-fold cross-validation. Model accuracy and receiver operating characteristic (ROC) curve scores served as the primary evaluation metrics. Results: KNN and ensemble bagging emerged as the most effective models, achieving accuracy rates of 100% and 98.3%, respectively, along with high area under the curve (AUC) values. These models excelled at identifying severe cases associated with intensive care unit (ICU) admission, ventilation, and metastatic cancer. Decision Trees demonstrated satisfactory performance with an accuracy of 82.55%, while SVM and Discriminant Analysis yielded moderate accuracy (64.26% and 65.11%). Naive Bayes underperformed, achieving only 40% accuracy, largely due to its assumption of feature independence. Conclusions: KNN and ensemble bagging models successfully predicted severe COVID-19 outcomes in cancer patients by capturing intricate relationships between factors such as ICU stays and ventilator support. Decision Trees also showed promise, but Naive Bayes was less reliable due to its simplified approach. The findings underscore the importance of non-linear models for predicting complex clinical outcomes. Despite robust evaluation through 10-fold cross-validation, the possibility of overfitting—particularly with KNN’s flawless accuracy—remains a concern. Additionally, the absence of external validation constrains the broader applicability of these findings to different patient populations.

The COVID-19 pandemic has significantly accelerated the adoption of telemedicine and digital health technologies, revealing their immense potential in managing cancer patients effectively. This article explores the impact of recent technological developments and widened consumer perspectives on personalised healthcare and patient awareness, particularly in oncology. Smartphones and wearable devices have become integral to daily life, promoting healthy lifestyles and supporting cancer patients through remote monitoring and health management. The widespread use of these devices presents an unprecedented opportunity to transform clinical trials and patient care by offering convenient and accessible means of collecting health data continuously and non-invasively. However, to fully harness their potential, it is crucial to establish standardised methods for measuring patient metrics to ensure data reliability and validity. This article also addresses the challenges of integrating these technologies into clinical practice, such as cost, patient and professional reluctance, and technological oversaturation. It emphasises the need for continuous innovation, the development of robust digital infrastructures, and the importance of fostering a supportive environment to integrate these advancements permanently. Ultimately, the convergence of technological innovation and personalised healthcare promises to enhance patient outcomes, improve quality of life, and revolutionise cancer management in the post-COVID era.

Steam superheaters experience various defects that can affect the system’s operation differently. Maintaining a constant temperature and pressure of the steam at the turbine input is crucial for optimal steam turbine running conditions. This is achieved by regulating the steam temperature in the superheater, which is divided into three parts with devices mounted at each connection point to allow for the injection of condensates to cool the steam. The steam pressure is controlled by adjusting the fuel flow command. However, controlling the output temperature can be challenging because of the transfer time delay between the points where the water is sprayed and the points where the steam temperature is measured. To address this challenge, a temperature control system was developed and tested in three different environments using an electronic simulator, the block-oriented simulation hardware BORIS, data acquisition board dSpace, and a MATLAB R2020a Simulink with a PI controller model. These simulations allowed for the study of the superheater’s function in both normal and fault conditions, with the possibility of controlling the system structure and detecting faults through the proper implementation of weighting matrices.

Robotic systems for lower limb rehabilitation are essential for improving patients’ physical conditions in lower limb rehabilitation and assisting patients with various locomotor dysfunctions. These robotic systems mainly integrate sensors, actuation, and control systems and combine features from bionics, robotics, control, medicine, and other interdisciplinary fields. Several lower limb robotic systems have been proposed in the patent literature; some are commercially available. This review is an in-depth study of the patents related to robotic rehabilitation systems for lower limbs from the point of view of the sensors and actuation systems used. The patents awarded and published between 2013 and 2023 were investigated, and the temporal distribution of these patents is presented. Our results were obtained by examining the analyzed information from the three public patent databases. The patents were selected so that there were no duplicates after several filters were used in this review. For each patent database, the patents were analyzed according to the category of sensors and the number of sensors used. Additionally, for the main categories of sensors, an analysis was conducted depending on the type of sensors used. Afterwards, the actuation solutions for robotic rehabilitation systems for upper limbs described in the patents were analyzed, highlighting the main trends in their use. The results are presented with a schematic approach so that any user can easily find patents that use a specific type of sensor or a particular type of actuation system, and the sensors or actuation systems recommended to be used in some instances are highlighted.

For people with amputated lower limbs, it is imperative to make high-performance prostheses that reproduce, as accurately as possible, the functions of the amputated limb. In this case, a preliminary study of the lower limbs from a kinematic and dynamic point of view is necessary. This paper proposes a prosthesis design and a system for acquiring the information needed to determine the stepping phase kinematic and dynamic parameters of the legs. This system consists of a sensory system attached to the legs and a acquisition data unit built around a microcontroller. The sensory system is based on a sensory system for determining the weight distribution on the sole, made of resistive pressure sensors. The sensory system will be subjected to measurement repeatability and homogeneity tests to evaluate and validate the accuracy and error of the proposed solution. The data obtained by the sensory system is transmitted in real-time, via wi-fi, to a computer system for interpretation. After processing and interpreting the data using standard data sets for comparison, the position of the legs, the type of gait and the phase of movement can be determined. Constructively, the system is configurable and can be adapted to any person, male or female, regardless of shoe size.