Automated cognitive health assessment in smart homes using machine learning

Javed, Abdul Rehman; Fahad, Labiba Gillani; Farhan, Asma Ahmad; Abbas, Sidra; Srivastava, Gautam; Parizi, Reza M.; Khan, Mohammad S.

doi:10.1016/j.scs.2020.102572

Cited by 134 publications

(90 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this block, there are several research studies that focus their analysis on e-health, health, and mobility and focus on the study of elderly people, home care, and disability, among others [ 77 , 78 , 79 ]. It is a very interesting block of study, especially taking into account the current pandemic and the need for protection and prevention in healthcare for the elderly and people with disabilities.…”

Section: Resultsmentioning

confidence: 99%

“…There is a lack of financial resources and greater multidisciplinarity and contacts between health governance and technological research centers. [77,78,[139][140][141][142][143][144][145][146][147] 4…”

Section: Discussion and Future Challengesmentioning

confidence: 99%

“…2021, 21, x FOR PEER REVIEW 16 of 39IoT Application Solutions[77,78,[139][140][141][142][143][144][145][146][147]:• Intelligent systems, sensors, IoT, technologically advanced medical devices, communications networks, etc., represent a significant improvement in the synergies between medical centers in vulnerable countries and advanced healthcare centers in developed countries.•In recent months, it has been proven that technological systems and IoT applications are of great importance in the research, application, and prevention of diseases and pandemics worldwide, when it comes to vaccine and drug manufacturing, logistics, population vaccination planning and management, data management, and patient monitoring at home, among other efforts.•Traffic accident prevention and control systems have an ally in technological systems and advanced IoT applications, for example, in user applications that inform and warn of vehicle congestion in cities and highways, accident warnings, alarm systems in adverse weather situations, among others.•Development of efficient and high-quality vaccines, where sensor applications, IoT, and intelligent systems play a great role. Technology, in all its fields, has a fundamental mission for the effective development and distribution of vaccines to the entire population, with applications to data management, including patient data, vaccination control, health and safety, among others.•In the area of air and soil pollution and the detection of chemical elements in the environment, there are multiple applications of sensors and intelligent systems that detect, transmit, manage, and prevent such problems.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals

Verdejo-Espinosa

Ruiz

Mata

et al. 2021

Sensors

View full text Add to dashboard Cite

We live in complex times in the health, social, political, and energy spheres, and we must be aware of and implement new trends in intelligent social health systems powered by the Internet of Things (IoT). Sustainable development, energy efficiency, and public health are interrelated parameters that can transform a system or an environment for the benefit of people and the planet. The integration of sensors and smart devices should promote energy efficiency and ensure that sustainable development goals are met. This work is carried out according to a mixed approach, with a literature review and an analysis of the impact of the Sustainable Development Goals on the applications of the Internet of Things and smart systems. In the analysis of results, the following questions are answered about these systems and applications: a) Are IoT applications key to the improvement of people’s health and the environment? b) Are there research and case studies implemented in cities or territories that demonstrate the effectiveness of IoT applications and their benefits to public health? c) What sustainable development indicators and objectives can be assessed in the applications and projects analyzed?

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussion and Future Challengesmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals

Verdejo-Espinosa

Ruiz

Mata

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Each base classifier performs a single binary classification task targeted towards detecting a single class from the rest [ 39 ]. The mathematical formulation of these classification methods is detailed in [ 40 – 44 ].…”

Section: Methodsmentioning

confidence: 99%

Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers

Fraiwan

Hassanin

2021

PLoS ONE

View full text Add to dashboard Cite

This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (HD). We further performed a statistical and classification comparison elucidating the discriminative capability of different gait signals, including vertical ground reaction force (VGRF), stride duration, stance duration, and swing duration. Feature representation of these gait signals was based on statistical amplitude quantification using the root mean square (RMS), variance, kurtosis, and skewness metrics. We investigated various decision tree (DT) based ensemble methods such as bagging, adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and random subspace to tackle the challenge of multi-class classification. Experimental results showed that AdaBoost ensembling provided a 6.49%, 0.78%, 2.31%, and 2.72% prediction rate improvement for the VGRF, stride, stance, and swing signals, respectively. The proposed approach achieved the highest classification accuracy of 99.17%, sensitivity of 98.23%, and specificity of 99.43%, using the VGRF-based features and the adaptive boosting classification model. This work demonstrates the effective capability of using simple gait fluctuation analysis and machine learning approaches to detect DNDs. Computer-aided analysis of gait fluctuations provides a promising advent to enhance clinical diagnosis of DNDs.

show abstract

“…Health maintenance and improvement are the key to living a healthy life [20,21,38,41,49], but the outbreak of COVID-19 has become the biggest threat to human existence. COVID-19 is a fatal widespread disease instigated by a recently discovered COVID-19 1 .…”

Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

Self Cite

View full text Add to dashboard Cite

Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease’s risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

show abstract

Automated cognitive health assessment in smart homes using machine learning

Cited by 134 publications

References 41 publications

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals

Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

Contact Info

Product

Resources

About