Introduction: Dementia is a progressive disorder associated with age, which is characterized by deterioration of individuals' cognitive functions such as the ability to perform routine tasks. With the increase of human life expectancy, the prevalence of dementia patients will reach 152 million in 2050. Unfortunately, there is no treatment available to cure dementia or alter the course of its progression. However, there is an area of support for patients and caregivers to assist daily living. Technological devices and applications are increasingly advancing, exploiting sensory data for dementia patients and homecare using smartphones to permit monitoring of their activities.
Aim: This paper uses the labeled dataset besides comparing the 3-classification algorithm to evaluate whether or not the algorithms deployed can classify the activities with high accuracy.
Results: A public data is used to classify human activities into one of the six activities, BigML platform is used to build machine learning models. Results show that machine learning algorithms can achieve high accuracy. The activity recognition algorithms are highly accurate using ridged regression and deep neural networks, with almost all activities being recognized correctly over 98% of the time.
Conclusion: An application of smartphones can be utilized for human activities monitoring by proposing a high level for dementia patients and homecare monitoring services. Using this service, the patients only need to carry the smartphone, and their caregivers simply need to use the application that monitors their patients' activities.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780789 | PMC |
| http://dx.doi.org/10.5455/aim.2020.28.196-201 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Drug Development.
Alzheimers Dement
December 2024
The University of Texas Health Science Center at Houston, Houston, TX, USA.
Background: Developing drugs for treating Alzheimer's disease (AD) has been extremely challenging and costly due to limited knowledge on underlying biological mechanisms and therapeutic targets. Repurposing drugs or their combination has shown potential in accelerating drug development due to the reduced drug toxicity while targeting multiple pathologies.
Method: To address the challenge in AD drug development, we developed a multi-task machine learning pipeline to integrate a comprehensive knowledge graph on biological/pharmacological interactions and multi-level evidence on drug efficacy, to identify repurposable drugs and their combination candidates RESULT: Using the drug embedding from the heterogeneous graph representation model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, mechanistic efficacy in preclinical models, population-based treatment effect, and Phase 2/3 clinical trials.
Background: In Alzheimer's Disease (AD) trials, clinical scales are used to assess treatment effect in patients. Minimizing statistical uncertainty of trial outcomes is an important consideration to increase statistical power. Machine learning models can leverage baseline data to create AI-generated digital twins - individualized predictions (or prognostic scores) of how each patient's clinical outcomes may change during a trial assuming they received placebo.
View Article and Find Full Text PDFDrug Development.
Alzheimers Dement
December 2024
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Background: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.
View Article and Find Full Text PDFDementia Care Research and Psychosocial Factors.
Alzheimers Dement
December 2024
Imperial College London, London, United Kingdom; UK Dementia Research Institute, Care Research and Technology Centre, London, United Kingdom.
Background: Close to 23% of unplanned hospital admissions for people living with dementia (PLWD) are due to potentially preventable causes such as severe urinary tract infections (UTIs), falls, and respiratory problems. These affect the well-being of PLWD, cause stress to carers and increase pressure on healthcare services.
Method: We use routinely collected in-home sensory data to monitor nocturnal activity and sleep data.
Dementia Care Research and Psychosocial Factors.
Alzheimers Dement
December 2024
Department of Psychology & Language Sciences, University College London, London, United Kingdom.
Background: Dysphagia is an important feature of neurodegenerative diseases and potentially life-threatening in primary progressive aphasia (PPA), but remains poorly characterised in these syndromes. We hypothesised that dysphagia would be more prevalent in nonfluent/agrammatic variant (nfv)PPA than other PPA syndromes, predicted by accompanying motor features and associated with atrophy affecting regions implicated in swallowing control.
Methods: In a retrospective case-control study at our tertiary referral centre, we recruited 56 patients with PPA (21 nfvPPA, 22 semantic variant (sv)PPA, 13 logopenic variant (lv)PPA).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!