Cognitive dysfunction affects the performance of Activities of Daily Living (ADL) and the quality of life of people with these deficits and their caregivers. To the knowledge of the authors, to date, there are few studies that focus on knowing the relationship between personal autonomy and deductive reasoning and/or categorization skills, which are necessary for the performance of the ADL. The aim of this study was to explore the relationships between ADL and categorization skills in older people. The study included 51 participants: 31 patients with cognitive impairment and 20 without cognitive impairment. Two tests were administered to assess cognitive functions: (1) the Montreal Cognitive Assessment (MoCA); and (2) the digital version of Riska Object Classification test (ROC-d). In addition, the Routine Tasks Inventory-2 (RTI-2) was applied to determine the level of independence in activities of daily living. People with cognitive impairment performed poorly in categorization tasks with unstructured information (p = 0.006). Also, the results found a high correlation between cognitive functioning and the performance of ADLs (Physical ADL: r = 0.798; p < 0.001; Instrumental ADL: r = 0.740; p < 0.001), a moderate correlation between Physical ADLs and categorization skills (unstructured ROC-d: r = 0.547; p < 0.001; structured ROC-d: r = 0.586; p < 0.001) and Instrumental ADLs and categorization skills in older people (unstructured ROC-d: r = 0.510; p < 0.001; structured ROC-d: r = 0.463; p < 0.001). The ROC-d allows the assessment of categorization skills to be quick and easy, facilitating the assessment process by OT, as well as the accuracy of the data obtained.
Background This article describes the development and evaluation of a distributed user interface (DUI) application to assess visuomotor organization ability. This application enables therapists to evaluate the acquired brain injury (ABI) on patients, and patients, to perform the assessment on a touch screen while therapists can observe the assessment process in real time on a separated monitor without interfering patients during the process as in traditional methodologies employing physical elements. Objectives The main goal of this research is the evaluation of the quality in use of DUIs in the Pegboard Construction assessment with patients with ABI from the therapist perspective in the area of occupational therapy. Methods To evaluate our system, we have performed a usability evaluation following the ISO/IEC 25010 and ISO/IEC 25062 standards to evaluate software usability and quality and it was conducted in collaboration with therapists and psychologists that have previously worked with people with ABI in diagnostic and assessment tasks. Results We show the results of the evaluation collected in a table that shows the completeness rate for each user for both, assisted (i.e., the percentage of tasks where participants performed with test director assistance) and unassisted tasks (i.e., the percentage of tasks where participants completed tasks autonomously), the total time participants required to complete proposed tasks, the number of mistakes participants performed during the session, and the number of assists they required to finish proposed tasks. In addition, we also evaluated the user satisfaction regarding our application using the system usability scale. Conclusion The use of information technologies in this field enables therapists to perform these evaluations in a simpler, efficient, and automated way. This proposal enables patients to perform the assessment as it is performed traditionally using paper providing them with a touch screen in which they can easily insert a set of pins into the holes. The usability evaluation of the proposal meets the appropriate design standards for applications of this type, and this is demonstrated by the high degree of satisfaction of the participants.
In this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class. Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 \% of Accuracy, 94 \% of Precision, 91 \%, of Recall and 92\% of F1-Score.
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