Many wheelchair people depend on others to control the movement of their wheelchairs, which significantly influences their independence and quality of life. Smart wheelchairs offer a degree of self-dependence and freedom to drive their own vehicles. In this work, we designed and implemented a low-cost software and hardware method to steer a robotic wheelchair. Moreover, from our method, we developed our own Android mobile app based on Flutter software. A convolutional neural network (CNN)-based network-in-network (NIN) structure approach integrated with a voice recognition model was also developed and configured to build the mobile app. The technique was also implemented and configured using an offline Wi-Fi network hotspot between software and hardware components. Five voice commands (yes, no, left, right, and stop) guided and controlled the wheelchair through the Raspberry Pi and DC motor drives. The overall system was evaluated based on a trained and validated English speech corpus by Arabic native speakers for isolated words to assess the performance of the Android OS application. The maneuverability performance of indoor and outdoor navigation was also evaluated in terms of accuracy. The results indicated a degree of accuracy of approximately 87.2% of the accurate prediction of some of the five voice commands. Additionally, in the real-time performance test, the root-mean-square deviation (RMSD) values between the planned and actual nodes for indoor/outdoor maneuvering were 1.721 × 10⁻⁵ and 1.743 × 10⁻⁵, respectively.

Left ventricular assist devices (LVADs) are emerging innovations that provide a feasible alternative treatment for heart failure (HF) patients to enhance their quality of life. In this work, a novel physiological control system to optimize LVAD pump speed using an H-infinity controller was developed. The controller regulates the calculated target pump flow vs. measured pump flow to meet the changes in metabolic demand. The method proposes the implementation of the Frank–Starling mechanism (FSM) approach to control the speed of an LVAD using the left ventricle end-diastolic volume (V_lved) parameter (preload). An operating point was proposed to move between different control lines within the safe area to achieve the FSM. A proportional–integral (PI) controller was used to control the gradient angle between control lines to obtain the flow target. A lumped parameter model of the cardiovascular system was used to evaluate the proposed method. Exercise and rest scenarios were assessed under multi-physiological conditions of HF patients. Simulation results demonstrated that the control system was stable and feasible under different physiological states of the cardiovascular system (CVS). In addition, the proposed controller was able to keep hemodynamic variables within an acceptable range of the mean pump flow (Qp) (max = 5.2 L/min and min = 3.2 L/min) during test conditions.

In this work, we developed a prototype that adopted sound-based systems for localization of visually impaired individuals. The system was implemented based on a wireless ultrasound network, which helped the blind and visually impaired to navigate and maneuver autonomously. Ultrasonic-based systems use high-frequency sound waves to detect obstacles in the environment and provide location information to the user. Voice recognition and long short-term memory (LSTM) techniques were used to design the algorithms. The Dijkstra algorithm was also used to determine the shortest distance between two places. Assistive hardware tools, which included an ultrasonic sensor network, a global positioning system (GPS), and a digital compass, were utilized to implement this method. For indoor evaluation, three nodes were localized on the doors of different rooms inside the house, including the kitchen, bathroom, and bedroom. The coordinates (interactive latitude and longitude points) of four outdoor areas (mosque, laundry, supermarket, and home) were identified and stored in a microcomputer’s memory to evaluate the outdoor settings. The results showed that the root mean square error for indoor settings after 45 trials is about 0.192. In addition, the Dijkstra algorithm determined that the shortest distance between two places was within an accuracy of 97%.

Mechanical ventilators are advanced life-supporting machines in this century. The ventilator needs to be safe, flexible, and easy for competent clinicians to use. Since ventilators supply the patient with gas, they need pneumatic components to be present. First technology ventilators were typically powered by pneumatic energy. Gas pressure is used to power ventilators as well as ventilate patients. Nowadays, ventilators are operated electronically with the useful microprocessor tool. This proposal aims to design a simple portable mechanical ventilator that includes measuring some important physiological variables such as respiratory rate, heart rate, and O2 saturation, which can be utilized in hospital and at home. The proposed system includes Arduino, Raspberry pi4, touch screen, and graphical user interface. This study showed a significant individual performance for measuring some important parameters such as flow rate, tidal volume, and minute ventilation. The accuracy of measuring the flow rate was 72%. The Cohen's kappa (CK) was estimated to be 0.61. The accuracy of calculated the tidal volume was estimated at 83% with 0.80 CK. The accuracy of measuring the O2 saturation was estimated at 99% with 0.99 CK. The advantages of the proposed design are cost-effective, safe, flexible, and easy to use. Also, this system is smart and can control its transactions, so it can be used at home without the need for professional help. The operating parameters can also be set by the user with a simple user interface.

Pediatric power wheelchairs (PWCs) are essential assistive technologies (ATs) that enhance independence and quality of life for children with disabilities. Despite the growing disability rights initiatives in Saudi Arabia, evidence on provider practices and service delivery gaps remains limited. This study offers the first national-level insights into pediatric PWC provision in Saudi Arabia. In this method, a national electronic survey was distributed to healthcare professionals, including physicians, occupational therapists, physical therapists, biomedical engineers, and equipment suppliers. A total of 203 valid responses were analyzed using descriptive and inferential statistics. The result providers with AT professional or seating and mobility specialist certification reported significantly higher PWC prescription rates. Adolescents (14-18 years) accounted for nearly 40% of prescriptions, while younger children were underrepresented, reflecting delays in access to early mobility. Key barriers included financial constraints (71%), documentation requirements (54%), and restrictive eligibility criteria (52%). The study provides novel evidence on how provider training, age-related trends, and systemic barriers shape pediatric PWC access in Saudi Arabia. The findings underscore the importance of expanding specialized AT training, streamlining clinical and administrative pathways, and addressing infrastructure and funding gaps. These reforms carry both clinical implications—improving early intervention and functional independence—and policy implications for achieving equitable access to mobility technologies under Saudi Vision 2030.