Processing of Human Motions using Cost Effective EEG Sensor and Machine Learning Approach

Waheed, Shaikh Abdul; Revathi, S.; Matheen, Mohammed Abdul; Lodhi, Amairullah Khan; Ashrafuddin, Mohammed; Maboobatcha, G.S.

doi:10.1109/caida51941.2021.9425088

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Highly advanced and various mobile apps combining more diverse technologies and healthcare devices will certainly emerge (Alam et al, 2018;Bohr & Memarzadeh, 2020). On the positive side, it has been confirmed that recent advances in technology increase the possibility of ensuring costeffectiveness in implementing cost-effective mobile app interventions (Waheed & Khader, 2017;Waheed et al, 2021). For health apps that target patients with chronic disease, the utmost attention is required for the precise design of the user interface.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of Korea’s Medication Management Mobile Application

Kim,

Hwang,

Lee

et al. 2023

SAGE Open

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There is a growing interest in digital healthcare to manage disease and improve medication compliance. In Korea, the health apps market continues to grow, however, little research has been done on their content and quality. A thorough assessment beyond the current star rating would ensure the safety and effectiveness of health apps. A systematic search was conducted in the Google Play and Apple App Store. The Korean version of the Mobile App Rating Scale (MARS) was used to access and systematically evaluate the contents of available medication management apps. Descriptions and technical information about the app were collected based on AppStore and the software company’s website. A total of 2,135 apps were identified and 39 apps were evaluated by four independent reviewers. Of the apps included, 17.9% were paid ones, at a cost between $2 and $4. The oldest was developed 11 years ago and 5 apps (12.8%) were released within the last year. The study results identified the features of apps used to manage medications, ranging from reminders to connecting to IoT devices. In addition, relationships were identified between the input variables studied, including health professionals, updates, and quality scores using MARS. The findings of this review highlight the importance of quality control with adequate features and continuous maintenance in health apps. In addition, to achieve effectiveness with good medication adherence, along with the inclusion of health professionals, close collaboration with all stakeholders in addition to the proper integration of newly updated technology is essential.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review of Korea’s Medication Management Mobile Application

Kim,

Hwang,

Lee

et al. 2023

SAGE Open

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“…As we know, emotions are integral parts of temperaments that can be quantified and analyzed to explore their complexity level. Because of this, a novel EEG-based approach was presented for processing emotions of a particular category of participants (Waheed et al, 2021). In line with the previous research, an EEG-based sensor was utilized to assess emotional regulation among the adult population who suffer from this disorder (Waheed & Abdul Khader, 2020).…”

Section: Related Workmentioning

confidence: 91%

Healthcare Solutions for Children Who Stutter Through the Structural Equation Modeling and Predictive Modeling by Utilizing Historical Data of Stuttering

Waheed

Khader

2021

SAGE Open

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Earlier studies established the role of demographic and temperamental features (DTFs) in the adaptation of childhood stuttering. However, these studies have been short on examining the latent interrelationships among DTFs and not utilizing them in predicting this disorder. This research article endeavors to examine latent interrelationships among DTFs in relation to childhood-stuttering. The purpose of the present is also to analyze whether DTFs can be utilized in predicting the likely risk of this speech disorder. Historical data on childhood stuttering was utilized for performing the invloved experiments of this research. “Structural-Equation-Modeling” (SEM) was applied to examine latent interrelationships among DTFs in relation to stuttering. The predictive analytics approach was employed to ensure whether DTFs of children can be utilized for predicting the likely risk of childhood-stuttering. SEM-based path analysis explored potential latent interrelationships among DTFs by separating them into categories of background and intermediate. By utilizing the same set of the DTFs, predictive models were able to classify children into stuttering and non-stuttering groups with optimal prediction accuracy. The outcomes of this study showed how the stuttering related historical data can be utilized in offering healthcare solutions for individuals with stuttering disorder. The outcomes of the present study also suggest that historical data on stuttering is a very rich source of hidden trends and patterns concerning this disorder. These hidden trends and patterns can be captured by applying a different type of structural and predictive modeling to understand the cause-and-effect relationship among variables in relation to stuttering. The SEM utilizes the cause-and-effect relationship among variables to explore latent-interrelationships between them. While predictive modeling utilizes the cause-and-effect relationship among variables to predict the possible risk of stuttering with optimal prediction accuracy.

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