Classification of Alzheimer’s Patients through Ubiquitous Computing

Nieto-Reyes, Alicia; Duque, Rafael; Montaña, José Luis; Lage, Carmen

doi:10.3390/s17071679

Cited by 14 publications

(15 citation statements)

References 23 publications

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“…Researchers at the Massachusetts Institute of Technology in Cambridge, for example, discovered in 2015 that fine-grained analysis of people's motor behaviour, revealed through their keyboard typing patterns on personal devices, could enable earlier diagnosis of Parkinson's disease 7 . A 2017 study suggests that measures of mobility patterns, such as those obtained from people carrying smartphones during their normal daily activities, can be used to diagnose early signs of cognitive impairment resulting from Alzheimer's disease 8 .…”

Section: Four Concernsmentioning

confidence: 99%

Four ethical priorities for neurotechnologies and AI

Yuste

Goering

Arcas

et al. 2017

Nature

339

217

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A man with a spinal-cord injury (right) prepares for a virtual cycle race in which competitors steer avatars using brain signals. COMMENT © 2 0 1 7 M a c m i l l a n P u b l i s h e r s L i m i t e d , p a r t o f S p r i n g e r N a t u r e . A l l r i g h t s r e s e r v e d .example. Moreover, researchers can already interpret a person's neural activity from functional magnetic resonance imaging scans at a rudimentary level 1 -that the individual is thinking of a person, say, rather than a car.It might take years or even decades until BCI and other neurotechnologies are part of our daily lives. But technological developments mean that we are on a path to a world in which it will be possible to decode people's mental processes and directly manipulate the brain mechanisms underlying their intentions, emotions and decisions; where individuals could communicate with others simply by thinking; and where powerful computational systems linked directly to people's brains aid their interactions with the world such that their mental and physical abilities are greatly enhanced.Such advances could revolutionize the treatment of many conditions, from brain injury and paralysis to epilepsy and schizophrenia, and transform human experience for the better. But the technology could also exacerbate social inequalities and offer corporations, hackers, governments or anyone else new ways to exploit and manipulate people. And it could profoundly alter some core human characteristics: private mental life, individual agency and an understanding of individuals as entities bound by their bodies.It is crucial to consider the possible ramifications now.The Morningside Group comprises neuroscientists, neurotechnologists, clinicians, ethicists and machine-intelligence engineers. It includes representatives from Google and Kernel (a neurotechnology start-up in Los Angeles, California); from international brain projects; and from academic and research institutions in the United States, Canada, Europe, Israel, China, Japan and Australia. We gathered at a workshop sponsored by the US National Science Foundation at Columbia University, New York, in May 2017 to discuss the ethics of neurotechnologies and machine intelligence.We believe that existing ethics guidelines are insufficient for this realm 2 . These include the Declaration of Helsinki, a statement of ethical principles first established in 1964 for medical research involving human subjects (go.nature.com/2z262ag); the Belmont Report, a 1979 statement crafted by the US National Commission for the Protection of Human Subjects of Biomedical and Behavioural Research (go.nature.com/2hrezmb); and the Asilomar artificial intelligence (AI) statement of cautionary principles, published early this year and signed by business leaders and AI researchers, among others (go.nature.com/2ihnqac).To begin to address this deficit, here we lay out recommendations relating to four areas of concern: privacy and consent; agency and identity; augmentation; and bias. Different nations and people of varying re...

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Section: Four Concernsmentioning

confidence: 99%

Four ethical priorities for neurotechnologies and AI

Yuste

Goering

Arcas

et al. 2017

Nature

339

217

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“…Additionally, motion features were significantly correlated with scores in the Mini-Mental State Examination [22], probably the commonest brief cognitive test and mostly used to track the progression of AD, which could imply a relationship between daily motion behavior and the stage of the disease. Previous works have been published focused on the staging of AD or other dementias employing sensor-based systems [23,24]. However, these works did not explore the potential of the CNNs to classify the stage of AD.…”

Section: Related Workmentioning

confidence: 99%

“…The dataset from the case study was introduced in [23]. In this experiment, data were collected from 35 patients with Alzheimer's disease carrying a mobile device in a small pocket placed and oriented by a neuropsychologist.…”

Section: Data Descriptionmentioning

confidence: 99%

“…In addition, we compared these results against a set of common supervised learning classifiers: Support-Vector Machines (SVMs), Random Trees (RTs) , Random Forest (RF), and Multilayer Perceptron (MLP) (with logistic activation). These models were originally compared in [23], in which the MLP was the most successful classifier. Table 1 shows the results obtained with the different models used for classifying the dataset.…”

Section: Model Evaluationmentioning

confidence: 99%

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A Convolutional Neural Network-Based Method for Human Movement Patterns Classification in Alzheimer’s Disease

Bringas

Salomón²,

Duque

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Alzheimer's disease (AD) constitutes a neurodegenerative pathology that presents mobility disorders as one of its earliest symptoms. Current smartphones integrate accelerometers that can be used to collect mobility data of Alzheimer's patients. This paper describes a method that processes these accelerometer data and a convolutional neural network (CNN) that classifies the stage of the disease according to the mobility patterns of the patient. The method is applied in a case study with 35 Alzheimer's patients, in which a classification success rate of 91% was obtained.

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“…An important aspect is diagnosing when a patient evolves from his or her current stage into the next one, which usually entails a complex physical examination carried by the patient's medical doctor. As an additional tool for the doctor to consider in taking that decision, [3,4] analyzed the movement patterns of Alzheimer's sufferers when moving freely in a daycare facility. The objective of those papers was supervised classification, so that given the movement patterns of a set of patients and their disease stage, the stage of other patients could be predicted based on their movement patterns.…”

Section: Introductionmentioning

confidence: 99%

Functional Symmetry and Statistical Depth for the Analysis of Movement Patterns in Alzheimer’s Patients

2021

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Black-box techniques have been applied with outstanding results to classify, in a supervised manner, the movement patterns of Alzheimer’s patients according to their stage of the disease. However, these techniques do not provide information on the difference of the patterns among the stages. We make use of functional data analysis to provide insight on the nature of these differences. In particular, we calculate the center of symmetry of the underlying distribution at each stage and use it to compute the functional depth of the movements of each patient. This results in an ordering of the data to which we apply nonparametric permutation tests to check on the differences in the distribution, median and deviance from the median. We consistently obtain that the movement pattern at each stage is significantly different to that of the prior and posterior stage in terms of the deviance from the median applied to the depth. The approach is validated by simulation.

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Classification of Alzheimer’s Patients through Ubiquitous Computing

Cited by 14 publications

References 23 publications

Four ethical priorities for neurotechnologies and AI

Four ethical priorities for neurotechnologies and AI

A Convolutional Neural Network-Based Method for Human Movement Patterns Classification in Alzheimer’s Disease

Functional Symmetry and Statistical Depth for the Analysis of Movement Patterns in Alzheimer’s Patients

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