(1) Background: The dynamics of hand tremors involve nonrandom and short-term motor patterns (STMPs). This study aimed to (i) identify STMPs in Parkinson’s disease (PD) and physiological resting tremor and (ii) characterize STMPs by amplitude, persistence, and regularity. (2) Methods: This study included healthy (N = 12, 60.1 ± 5.9 years old) and PD (N = 14, 65 ± 11.54 years old) participants. The signals were collected using a triaxial gyroscope on the dorsal side of the hand during a resting condition. Data were preprocessed and seven features were extracted from each 1 s window with 50% overlap. The STMPs were identified using the clustering technique k-means applied to the data in the two-dimensional space given by t-Distributed Stochastic Neighbor Embedding (t-SNE). The frequency, transition probability, and duration of the STMPs for each group were assessed. All STMP features were averaged across groups. (3) Results: Three STMPs were identified in tremor signals (p < 0.05). STMP 1 was prevalent in the healthy control (HC) subjects, STMP 2 in both groups, and STMP3 in PD. Only the coefficient of variation and complexity differed significantly between groups. (4) Conclusion: These results can help professionals characterize and evaluate tremor severity and treatment efficacy.

Parkinson’s disease (PD) is a neurological disorder that is progressive and causes losses of dopaminergic neurons from the substantia nigra, a region in the human brain. The decrease of dopamine in this area elucidates the presence of motor symptoms, such as tremors, bradykinesia, rigidity, gait impairment, and non-motor symptoms, e.g., depression, loss of cognitive functions, sleep problems, and nerve pain. Among the motor symptoms, tremors can have the most impact on the social activities of people with PD. Furthermore, there is difficulty in diagnosing the underlying disorder that causes tremors. Thus, the study and development of methods to assess tremors and their severity is of paramount relevance for clinical practice. A typical clinical tool to evaluate tremor severity is the analysis of hand drawing shapes (e.g., spirals, circles, meanders, waves). The evaluation of these drawings is dependent on the experience of professionals, yielding a high variability of results. Aiming to contribute to the objective evaluation of hand drawing shapes of people with PD, this research proposes the application of the Random Forest Classifier to classify Histograms of Oriented Gradients (HOG) estimated from sinusoidal patterns collected from healthy individuals (n = 12) and from people with PD (n = 15). The highest accuracy, sensitivity and specificity classification success rates were of 83%, 85% and 81%, respectively. These results can be relevant for the early detection of pathological tremors, the follow-up of medical treatments and the diagnosis of parkinsonian conditions.

Background: Parkinson’s disease (PD) is a neurodegenerative condition of the central nervous system that causes motor and non-motor dysfunctions. The disease affects 1% of the world population over 60 years and remains cureless. Knowledge and monitoring of PD are essential to provide better living conditions for patients. Thus, diagnostic exams and monitoring of the disease can generate a large amount of data from a given patient. This study proposes the development and usability evaluation of an integrated system, which can be used in clinical and research settings to manage biomedical data collected from PD patients. Methods: A system, so-called Sistema Integrado de Dados Biomédicos (SIDABI) (Integrated Biomedical Data System), was designed following the model-view-controller (MVC) standard. A modularized architecture was created in which all the other modules are connected to a central security module. Thirty-six examiners evaluated the system usability through the System Usability Scale (SUS). The agreement between examiners was measured by Kendall’s coefficient with a significance level of 1%. Results: The free and open-source web-based system was implemented using modularized and responsive methods to adapt the system features on multiple platforms. The mean SUS score was 82.99 ± 13.97 points. The overall agreement was 70.2%, as measured by Kendall’s coefficient (p < 0.001). Conclusion: According to the SUS scores, the developed system has good usability. The system proposed here can help researchers to organize and share information, avoiding data loss and fragmentation. Furthermore, it can help in the follow-up of PD patients, in the training of professionals involved in the treatment of the disorder, and in studies that aim to find hidden correlations in data.

As the number of scientific publications continues to grow, their content becomes more extensive and research increasingly fragmented. In this context, scientific mapping becomes an essential practice. Bibliometrics offers valuable indicators that can guide studies and help build a coherent set of relevant works. This study highlights the main bibliometric indicators in the field of computer vision, specifically related to the monitoring and recording of student attendance in academic environments. Additionally, it conceptualises the key aspects of bibliometrics and outlines the study scenario. The metadata were extracted from the Web of Science and Scopus databases, considering all publications up to 2024. The results indicate that India is among the most prolific countries in this domain, and the terms "face recognition" and "computer vision" are the most frequently associated. The paper also presents the most cited articles, prominent authors, and consolidates a list of significant works in the area. Furthermore, the search strategy revealed a limited number of studies addressing spoofing, presentation attack detection (PAD), or liveness detection, suggesting a promising direction for future research in strengthening the reliability of attendance validation systems.

The volume of data for monitoring wellbeing and health of individuals, and the number of devices used to perform tests in patients remotely, have grown substantially. Following this trend, over the past years we have seen an increase in the number of studies reporting the monitoring of the cardinal signs (e.g., tremor, stiffness and bradykinesia) of Parkinson’s disease (PD) during prolonged activities for hours or days. A major challenge in the area is to monitor the progress of the disorder objectively so that treatments can be customized. In addition, patients suffer from the lack of predictive information regarding their health condition in the future. In this context, customized systems, i.e., database, that can group information from the motor symptoms of PD are of paramount relevance. By using such systems, one can track the progress of the disorder and more importantly can use data mining for seeking hidden patterns in the data. To contribute for the organization and management of information obtained from patients with PD this research proposed the architecture and organization of a multiplatform system with customized user control, modules and permissions to manipulate information on each screen. This system has three modules, being the first for storing and organizing information from data collection with distinct types of data; the second for the management of information from the application of the Unified Parkinson’s Disease Rating Scale (UPDRS); and the third for promoting technological innovation in the area. These three modules in a single system can be part of the clinical routine of hospitals and research centers dedicated to the understanding, treatment and research in PD.