Assessing cognitive function among older adults using machine learning and wearable device data: a feasibility study

• URL: http://arxiv.org/abs/2309.07133v2
• Date: Sun, 24 Mar 2024 23:12:01 GMT
• Title: Assessing cognitive function among older adults using machine learning and wearable device data: a feasibility study
• Authors: Collin Sakal, Tingyou Li, Juan Li, Xinyue Li,
• Abstract summary: We developed prediction models to differentiate older adults with normal cognition from those with poor cognition. Activity and sleep parameters were also more strongly associated with processing speed, working memory, and attention compared to other cognitive fluency.
• Score: 3.0872517448897465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Timely implementation of interventions to slow cognitive decline among older adults requires accurate monitoring to detect changes in cognitive function. Data gathered using wearable devices that can continuously monitor factors known to be associated with cognition could be used to train machine learning models and develop wearable-based cognitive monitoring systems. Using data from over 2,400 older adults in the National Health and Nutrition Examination Survey (NHANES) we developed prediction models to differentiate older adults with normal cognition from those with poor cognition based on outcomes from three cognitive tests measuring different domains of cognitive function. During repeated cross-validation, CatBoost, XGBoost, and Random Forest models performed best when predicting cognition based on processing speed, working memory, and attention (median AUCs >0.82) compared to immediate and delayed recall (median AUCs >0.72) and categorical verbal fluency (median AUC >0.68). Activity and sleep parameters were also more strongly associated with processing speed, working memory, and attention compared to other cognitive subdomains. Our work provides proof of concept that wearable-based cognitive monitoring systems may be a viable alternative to traditional methods for monitoring processing speeds, working memory, and attention. We further identified novel metrics that could be targets in future causal studies seeking to better understand how sleep and activity parameters influence cognitive function among older adults.

Related papers

• Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities [8.032202552952299]
  We present an intelligent conversational system for entertaining elderly people with news of their interest that monitors cognitive impairment transparently. We create dialogue flows automatically from updated news items using Natural Language Generation techniques. The system infers the gold standard of the answers to the questions, so it can assess cognitive capabilities automatically.
  arXiv  Detail & Related papers  (2024-05-28T19:17:48Z)
• Modeling User Preferences via Brain-Computer Interfacing [54.3727087164445]
  We use Brain-Computer Interfacing technology to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. We link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.
  arXiv  Detail & Related papers  (2024-05-15T20:41:46Z)
• In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment [0.8426358786287627]
  The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data. Our statistical analysis comparing drivers with mild cognitive impairment (MCI) to those without reveals that those with MCI exhibit smoother and safer driving patterns. Our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.
  arXiv  Detail & Related papers  (2023-11-15T15:47:24Z)
• Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data [67.60224656603823]
  Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time. Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles. Mobile passively sensed data captured from smartphones constitute an excellent alternative to profile patients' biorhythm.
  arXiv  Detail & Related papers  (2022-11-08T17:29:40Z)
• Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning [2.741266294612776]
  This research presents a novel self-supervised method for mental workload modelling from EEG data. The method is a convolutional recurrent neural network trainable with spatially preserving spectral topographic head-maps from EEG data to fit the brain rate variable. Findings point to the existence of quasi-stable blocks of learnt high-level representations of cognitive activation because they can be induced through convolution and seem not to be dependent on each other over time, intuitively matching the non-stationary nature of brain responses.
  arXiv  Detail & Related papers  (2022-09-21T07:44:21Z)
• Learning Human Cognitive Appraisal Through Reinforcement Memory Unit [63.83306892013521]
  We propose a memory-enhancing mechanism for recurrent neural networks that exploits the effect of human cognitive appraisal in sequential assessment tasks. We conceptualize the memory-enhancing mechanism as Reinforcement Memory Unit (RMU) that contains an appraisal state together with two positive and negative reinforcement memories.
  arXiv  Detail & Related papers  (2022-08-06T08:56:55Z)
• Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data [52.40058724040671]
  Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
  arXiv  Detail & Related papers  (2021-10-19T11:45:01Z)
• Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity [9.015698823470899]
  We present an approach to model functional brain connectivity across space and time. We use the Human Connectome Project dataset on sex classification and fluid intelligence prediction. Results show a prediction accuracy of 94.4% for sex, and an improvement of correlation with fluid intelligence of 0.325 vs 0.144, relative to a baseline model that encodes space and time separately.
  arXiv  Detail & Related papers  (2021-09-07T14:23:34Z)
• Patch-based Brain Age Estimation from MR Images [64.66978138243083]
  Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. We develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator.
  arXiv  Detail & Related papers  (2020-08-29T11:50:37Z)
• AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment [2.7998963147546148]
  AutoCogniSys is a context-aware automated cognitive health assessment system. We develop an automatic cognitive health assessment system in a natural older adults living environment. The performance of AutoCogniSys attests max. 93% of accuracy in assessing cognitive health of older adults.
  arXiv  Detail & Related papers  (2020-03-17T01:44:59Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.