Efficient EOG-based movement classification in IoMT using machine learning algorithms for people with motor disabilities.
In this paper, we present an Internet of Medical Things (IoMT)-based platform that depends on electrooculography (EOG) to assist, control, and monitor a smart home environment in real time for patients with motor disabilities. Users can interact with the intelligent environment through a Graphical U...
| Publicado en: | Disability & Rehabilitation: Assistive Technology Vol. 21; no. 5; pp. 1769 - 1812 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jul2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195930970&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195930970 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17483107 1X04 jtl: Disability & Rehabilitation: Assistive Technology issn: 17483107 maglogo: Y pubinfo: dt: Jul2026 vid: 21 iid: 5 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 195930970 193135710 195930970 195930970 10.1080/17483107.2025.2599995 195930970 ppf: 1769 ppct: 43 formats: tig: atl: Efficient EOG-based movement classification in IoMT using machine learning algorithms for people with motor disabilities. aug: au: El-Gindy, Saly Abd-Elateif El-Shafai, Walid Soliman, Naglaa F. Alkanhel, Reem Algarni, Abeer D. Abd El-Samie, Fathi E. affil: High Institute for Engineering & Technology-Al Obour, Al Obour K 21Cairo/Belbeis Rd., Egypt sug: subj: Machine Learning Algorithms Motor Skills Disorders Rehabilitation Internet of Things Classification Algorithms Electrooculography Methods Assistive Technology Neuromuscular Diseases Rehabilitation Signal Processing, Computer Assisted Eye Movements Classification Human Convolutional Neural Networks Wearable Sensors Home Environment Neural Networks (Computer) Support Vector Machine User-Computer Interface Assistive Technology Services Activities of Daily Living Sensitivity and Specificity Deep Learning Artificial Intelligence Comparative Studies Motivation Functional Status Data Analysis, Statistical ab: In this paper, we present an Internet of Medical Things (IoMT)-based platform that depends on electrooculography (EOG) to assist, control, and monitor a smart home environment in real time for patients with motor disabilities. Users can interact with the intelligent environment through a Graphical User Interface (GUI) that offers predefined options for controlling doors, windows, lights, air conditioning, temperature, and TV functions. The proposed approach is based mainly on the utilisation of two transforms, namely Stockwell transform (S-transform) and wavelet transform, for detection of abrupt changes in EOG signals. Several statistical attributes of the processed EOG signals are utilised to characterise them in order to detect each eye movement. Two different wavelet families, namely Daubechies (db4) and Symlets (Sym4), are considered. Finally, the data is classified using three types of Machine Learning (ML) algorithms in addition to a Deep Learning (DL) algorithm, namely Support Vector Machines (SVM), Kernel Neural Networks (KNN), Ensemble Tree (ET), and Convolutional Neural Networks (CNN) classifiers. The proposed approach reveals the best results in comparison with the results of previous methods. A high average accuracy of 97.7% is achieved with the SVM classifier using the db4 wavelet, while an accuracy of 95.75% is achieved with the Sym4 wavelet, which indicates that the db4 wavelet gives the best results. IMPLICATIONS FOR REHABILITATION: Psychosocial Support: Addressing mental health needs through counselling and support groups to boost self-esteem, reduce isolation, and foster social connections. Skills Training and Vocational Rehabilitation: Programs that provide training for employment or self-employment, increasing economic independence and social integration. Accessibility and Inclusivity: Ensuring physical spaces, resources, and services are accessible and accommodating, fostering greater participation in all aspects of life. Family and Caregiver Involvement: Equipping families and caregivers with the knowledge and tools to support rehabilitation goals, creating a supportive environment for long-term progress. Research helps people with disabilities achieve their mobility desires, provides a quick way for people with disabilities to interact with machines and makes it easier for people with disabilities to have a better life by using technology to interpret eye movements. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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