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.
BackgroundCognitive impairment is one of the characteristics shown by patients with Alzheimer's dementia (Montenegro et al., 2020; Kelley & Petersen, 2007). This affects the executive functions of most of these patients from the early stage (Guarino et al., 2019) and consequently, have difficulty in carrying out their daily activities (Dubbelman et al., 2020). These activities require precise coordination of the person's movements and interaction with their environment, so instrumental activities of daily living (IADLs) are predictors of such impairment (Barberger‐Gateau & Dartigues, 1993), as several research studies have shown(Brovelli et al., 2017). Therefore, assessing the performance of IADLs by analyzing human biomechanical markers such as hand movements is of great interest. This, together with the application of artificial intelligence techniques, in particular deep learning, will allow the development of technological tools for the creation of an automated model to support the early diagnosis of Alzheimer's disease.MethodThis work was inspired by the development of Alejandro Acosta et al, where human biomechanical markers were analyzed throughout the performance of IADL activities to recognize the human functional pattern (Acosta‐Franco et al., 2021). To improve the accuracy of the baseline model, we propose a hand detection algorithm based on a probabilistic approach considering skin color as a descriptor. To test the proposed algorithm, we used the dataset of egocentric videos containing the performance of IADL activities, which is organized into four classes, based on the prehensile patterns of the hands: force and precision, and on the kinematics of the instruments: displacement and manipulation.ResultWith the improvement made to the base model we obtained an accuracy higher than 74% in recognizing movement patterns of displacement and manipulation of objects, and a good prediction of the depth of the anatomical planes.ConclusionOur improved model obtained a good percentage of recognition accuracy for the instrument's kinematics, which can help 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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