El dominio del lenguaje escrito se basa en la consolidación de representaciones neurales complejas de los patrones ortográficos de las palabras. En virtud de explorar las características presentes en procesos neurales relacionados con la especialización ortográfica, se estudiaron con métodos de imagen por resonancia magnética funcional los niveles neurales de activación intrahemisférica de 27 jóvenes con alto y bajo rendimiento ortográfico mientras ejecutaban tareas ortográficas. Los resultados sugieren que, en participantes con alto rendimiento ortográfico, la intensidad de activación neural en el hemisferio izquierdo no difiere ante el estímulo de palabras o pseudohomófonos (palabras con error ortográfico); en cambio, difieren cuando se emiten respuestas correctas vs. incorrectas. En participantes con bajo rendimiento ortográfico no se encontró diferencia interhemisférica en ninguna tarea. Se encontró tendencia de que los participantes con bajo rendimiento ortográfico tienen mayor dispersión de la activación neural, respecto a los participantes con alto rendimiento, en el hemisferio derecho e izquierdo, además, en participantes con bajo rendimiento ortográfico se encontró tendencia de que la detección consciente aumenta la variación de la activación neural; sin embargo, no se encontró evidencia estadística concluyente. Este estudio ha abonado a la evidencia de la existencia de la especialización neural orientada a habilidades ortográficas.
Background Alzheimer's Disease (AD) diagnosis at early stages currently represents an important challenge for the scientific community, which is gradually accentuated due to the global perspective of population aging. Current clinical processes for the diagnosis of this disease are increasingly effective; these include invasive tests of nervous system biomarkers, which are complemented by non‐invasive tests of human cognitive and functional performance, such as the mini‐mental state examination and the analysis of Instrumental Activities of Daily Living (IADLs). Method The present work is centered around the development of technological tools for creating an automated model to support the diagnosis of early AD disease. We present a novel non‐invasive methodology for the development of an Artificial Intelligence‐based model, which analyzes human biomechanical markers of IADLs activities to recognize human functional patterns. For the development of this model, we have built a dataset of egocentric videos containing IADLs activities, organized in four classes, based on the prehensile patterns of the hands: strength and precision, and on the kinematics of the instruments: displacement and manipulation. We have characterized the dataset using mathematical methods to get information to directly emulate the relationship with Lawton and Brody's geriatric test, which is used in clinical protocols to estimate human functional capacity. This characterization relationship between biomechanical markers and human functional patterns represents a benefit for quantitative and objective assessment in support of geriatric evaluation and patient follow‐up. Result Our proposed model results in an accuracy of 73.74% in the recognition of human functional patterns related to the kinematics of the instruments, 59.84% in the analysis of the prehensile pattern of the hands, and 48.5% when the classes were recognized independently. Conclusion This allows us to establish in a quantifiable way performance region benchmarks of human functional capacity for IADLs activities, by obtaining a support model in the diagnostic evaluation of AD disease at early stages. Our proposed model allows us to establish the guideline to improve the automatic recognition of human functional patterns, of which we obtained an acceptable percentage testing instruments kinematics', followed by the hands' prehensile patterns.
Background Alzheimer’s disease is a neurodegenerative disorder (Vally & Kathrada, 2019), the most common type of dementia in the elderly population (Kim, 2020) and is a great challenge in the geriatric field. Cognitive impairment is one of the features shown by Alzheimer’s dementia patients, and instrumental activities of daily living (IADL) are predictors of cognitive impairment as proven by several research works. Over the past few years, deep learning has had a tremendous improvement effect on diverse science areas, inclusive of healthcare (Farouk & Rady, 2020). This study aims to explore a non‐invasive and novel approach with the use of deep learning towards the early diagnosis of Alzheimer’s disease. Method To measure cognitive impairment, IADL are objectively recorded, capturing data from egocentric videos using wearable cameras that are attached to a glass frame of each participant, mainly focusing on hands use while performing these activities. Obtained images are analyzed based on human‐object interaction and human‐environment interaction. To make precise analysis while performing these activities, we propose the use and relationship of the anatomical planes (coronal, sagittal and transverse planes) and healthy human functional patterns [Martínez‐Velilla, 2018], in a quantitative way using deep learning. In the coronal plane, patterns involving displacement movements and object manipulation were identified with an accuracy of 87%. The information from the sagittal and transverse planes is developed with a deep learning model, which provides the required depth data to link the IADL's quality. By analyzing these planes, we can get more information about the distance of the hand and body motion while performing these activities. Result From our work, we obtained an accuracy of 87% recognizing movement patterns of displacement and object manipulation, and a good prediction of the depth of the anatomical planes. Conclusion Our model serves as a tool for proactive prediction of Alzheimer’s dementia and support in clinical decision‐making.
Good health and functional ability are important for individuals to lead fulfilling mental, psychological, and social lives. The diseases such as Dementia causes irreversible damage, decline in cognition, function, and behavior which translates into difficulty in independently performing daily tasks. Studies showed that assessment of Instrumental activities of daily living(IADLs) correlate with humans' cognitive and functional status. Analysis of biomechanical markers such as hand movement/use was done with artificial intelligence(AI). We present an optimized AI algorithm for hand detection in the analysis of egocentric video recordings. This improved AI algorithm is based on a probabilistic approach where hand regions are detected in egocentric videos. They then feed the human functional pattern recognition process. To evaluate the performance of our proposal we use a dataset containing the four functional patterns organized into four classes, based on the prehensile patterns of the hands: strength-precision, and on the kinematics of the instruments: displacementhandling. This work was inspired by a previous work done by our group, where biomechanical markers were analyzed throughout the performance of IADL activities to recognize the human functional pattern. The result of our proposal yielded an accuracy of 87.5% in recognizing strength-precision and displacement-handling movement patterns when evaluating the test database with information from Segmented and Not-Segmented videos. This resulted in a single video that changed its classification ratio between the two subsets. This can be of great potential in the development of technological tools for the creation of an automated model to support the diagnosis of early Alzheimer's disease.
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