Browsing by Author "Morufu Olusola Ibitoye"

Now showing 1 - 4 of 4
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    Development of an Electrically Powered Medical Suction Device for Clinical Applications in Developing Countries
    (JOURNAL OF SCIENCE TECHNOLOGY AND EDUCATION, 2024) Morufu Olusola Ibitoye; Olufunke Mary Oderemi; Suleiman Abimbola Yahaya
    In clinical settings, the process of suction is the removal of biological fluids using vacuum technology. This study sought to design and develop an inexpensive suction device for the facilitation of airway management in patients under emergency or critical care. The suction device was fabricated based on the design specifications. The device was designed to be able to aspirate biological samples at low (70 mmHg), medium (90 mmHg), and high (120 mmHg) pressures using catheters of 9.4 mm and 3.3 mm diameters. These pressures were selected to enable the device to be useful for infants and elderly patients. The developed device passed the required electrical safety tests using the standard electrical safety analyzer by Fluke. For example, the leakage AC and DC were 0.1 µA AC and 0.0 µA DC, respectively, suggesting that the device is safe for use on patients. We are confident that the introduction of this inexpensive device (
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    Effectiveness of FES-supported leg exercise for promotion of paralysed lower limb muscle and bone health—a systematic review
    (De Gryter Biomedical Engineering / Biomedizinische Technik, 2023) Morufu Olusola Ibitoye; Nur Azah Hamzaid; Yusuf Kola Ahmed
    Leg exercises through standing, cycling and walking with/without FES may be used to preserve lower limb muscle and bone health in persons with physical disability due to SCI. This study sought to examine the effectiveness of leg exercises on bone mineral density and muscle cross-sectional area based on their clinical efficacy in persons with SCI. Several literature databases were searched for potential eligible studies from the earliest return date to January 2022. The primary outcome targeted was the change in muscle mass/volume and bone mineral density as measured by CT, MRI and similar devices. Relevant studies indicated that persons with SCI that undertook FES- and frame-supported leg exercise exhibited better improvement in muscle and bone health preservation in comparison to those who were confined to frame-assisted leg exercise only. However, this observation is only valid for exercise initiated early (i.e., within 3 months after injury) and for ≥30 min/day for ≥ thrice a week and for up to 24 months or as long as desired and/or tolerable. Consequently, apart from the positive psychological effects on the users, leg exercise may reduce fracture rate and its effectiveness may be improved if augmented with FES.
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    Investigation of Optimal Components and Parameters of the Incremental PCA-based LSTM Network for Detection of EEG Epileptic Seizure Events
    (Bima Journal of Science and Technology, 2024) Sani Saminu; Adamu Halilu Jabire; Hajara Abdulkarim Aliyu; Adamu Ya’u Iliyasu; Suleiman Abimbola Yahaya; Morufu Olusola Ibitoye; Guizhi Xu
    Prediction of Epileptic seizures is highly imperative to improve the epileptic patient’s life. Epileptic seizures occur due to brain cells excessive abnormal activity that leads to unprovoked seizures and may occur without prior notice. Therefore, preventive measure that monitor and alert the possible occurrence of the seizures is paramount. Commercial and clinical available epileptic seizure computer aided detection system that utilized deep learning algorithms suffers from many challenges. These challenges ranges from low accuracy and precision, sensitive to artifacts and noise, among others. To enhance and increase the accuracy and optimal performance of these networks, this paper endeavor to investigate various optimization algorithm to optimized the network components and parameters in the developed incremental Principal Components Analysis based Long Short-Term Memory (Inc-PCA-LSTM) network for the detection and classification of Electroencephalograph (EEG) epileptic seizure signals based on the big data scenario. The model proved to be effective in the characterization of seven seizure events. The Adam, Elu, Orthogonal, and L1/L2 performed better than their counterparts in optimization functions, activation functions, initialization functions, and regularisation techniques respectively. The accuracy values of 97.5%, 97.5%, 98.4%, and 98.5% was obtained for each of the mentioned core components receptively.
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    Posture Monitoring Device for Abnormal Spine Musculoskeletal Detection Using Flex Sensor
    (2022) Morufu Olusola Ibitoye; Yusuf Kola Ahmed; Taiye Mary Ajibola; Idowu Olayemi Oladejo
    During activity of daily living involving standing, sitting or maneuvering, humans are typically susceptible to neck and shoulder strain, and other musculoskeletal conditions, severity of which may lead to posture deformity. To prevent these clinical conditions, we developed a low-cost and user-friendly posture monitor mainly built on a single flex sensor. The design was based on abnormal posture detection by a flex sensor whose signal triggers a buzzer to notify the user or care giver of these clinical conditions. Through Arduino IDE, an ATmega328 microcontroller controlled the sequence of the flex sensing to the audible alarm output. The buzzer was designed to go off automatically when the user adjusts his/her posture to normalcy. With a sensitivity of 84.6% during testing of this device, the developed posture monitoring device could be adjudged effective and suitable to monitor people's posture while sitting or standing especially in environments where menial jobs are prevalent. In these individuals, this device may prevent deformity prone diseases that may warrant surgical intervention for correction.