Detection of Motor Seizures and Falls in Mobile Application using Machine Learning Classifiers

Zia, Shafaq; Khan, Ali Nawaz; Mukhtar, Mayyda; Ali, Shan E; Shahid, Jibran; Sohail, Mobeen

doi:10.1109/iaict50021.2020.9172028

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…There have been some previous proposals for mobile applications based on ML, all developed from 2016 onwards [5,[22][23][24][25][26]. The limitations listed by these studies include insufficient observations from physicians, lack of publicly available data, unavailability of internet connection in remote areas and lack of reliability of sources that provided the data…”

Section: Discussionmentioning

confidence: 99%

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study

et al. 2022

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Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.

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Section: Discussionmentioning

confidence: 99%

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study

et al. 2022

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“…Human Activity Recognition (HAR) through wearable sensors is gaining attention due to progress in automated feature extraction, classification, mobile and cloud computing for a number of applications [1] [2]. HAR is an ability to interpret human body gesture or motion via sensors such as accelerometery sensors, heart rate monitors and EEG etc.…”

Section: Introductionmentioning

confidence: 99%

“…Data of an individual's body parameters can be extracted using wearables and mobile devices and it can be further processed for the classification of ADLs using machine learning algorithms and deep learning such as Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), Decision Trees (DT), Pruned Decision tree (J48), Random Forest (RF) , Logistic Model Trees (LMT), Bayesian Network (BN), Naïve Bayes (NB), Multilayer Perception (MLP), Instance Based Learning (IBL), and Support Vector Machine Learning (SVM) [9]. Mentioned Machine Learning classifiers and deep learning techniques can be used to improve the classification and detection accuracy of HAR, and abnormal activities including seizure and Fall detection [2] [10].…”

Section: Introductionmentioning

confidence: 99%

Detection of Generalized Tonic Clonic Seizures and Falls in Unconstraint Environment Using Smartphone Accelerometer

et al. 2021

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The detection of Generalized Tonic Clonic Seizures (GTCS) and Falls is of utmost importance due to the increase in prevalence of epilepsy and Sudden Death in Epileptic Patients during CoVID-19 pandemic, and prevention of serious injuries in Fall risk groups such as elderly requiring continuous monitoring for disease management and assisted living etc. Monitoring of Activities of Daily Living (ADLs) can assist in the detection of symptoms and onset of neurological disorders such as Alzheimer's, stroke, and epileptic seizures. With a host of embedded sensors, improved memory, enhanced processing capabilities and availability to masses, smartphones can be used for Human Activity Recognition (HAR) through continuous monitoring of ADLs. This paper presents a tri-axial accelerometer-based approach to detect and classify activities performed by individuals by applying machine learning algorithms including RF, J48, NB, LMT and SVM to movement data. Movement data is collected in real-time from the embedded accelerometer of a smartphone worn by individual on upper-left arm in unconstraint environment. It is pre-processed using time and frequency domain analysis and spatial domain features are computed. Supervised machine learning techniques are applied to classify ADLs into five classes based on the intensity of movements: Stationary, Light Ambulatory, Intense Ambulatory, GTCS and Falls. We also used training data from MyNeuroHealth dataset collected from 23 individuals including epilepsy patients. Based on gathered results, Random Forest outperforms other classifiers with classification accuracy of 99.6% for stationary, 81.5% for light ambulatory, 99.8% for intense ambulatory and GTCS, and 97.2% for Falls corresponding to training data of 14000 samples. To date, activity classification in our system has been implemented on cloud instead of mobile phone application as subjects are using smartphones with dissimilar software and hardware specifications for assisted living applications.

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Comparative Analysis of e-Health Care Telemedicine System Based on Internet of Medical Things and Artificial Intelligence

Kadu

Singh

2021

2021 2nd International Conference on Smart Electronics and Communication (ICOSEC)

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Detection of Motor Seizures and Falls in Mobile Application using Machine Learning Classifiers

Cited by 3 publications

References 14 publications

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study

Detection of Generalized Tonic Clonic Seizures and Falls in Unconstraint Environment Using Smartphone Accelerometer

Comparative Analysis of e-Health Care Telemedicine System Based on Internet of Medical Things and Artificial Intelligence

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