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.

Installing and developing a sophisticated control system to optimize left ventricular assist device (LVAD) pump speed to meet changes in metabolic demand is essential for advancing LVAD technology. This paper aims to design and implement a physiological control method for LVAD pumps to provide optimal cardiac output. The method is designed to adjust the pump speed by regulating the pump flow based on a predefined set point (operating point). The Frank–Starling mechanism technique was adopted to control the set point within a safe operating zone (green square), and it mimics the physiological demand of the patient. This zone is predefined by preload control lines, which are known as preload lines. A proportional–integral (PI) controller was utilized to control the operating point within safe limits to prevent suction or overperfusion. In addition, a PI type 1 fuzzy logic controller was designed and implemented to drive the LVAD pump. To evaluate the design method, rest, moderate, and exercise scenarios of heart failure (HF) were simulated by varying the hemodynamic parameters in one cardiac cycle. This evaluation was conducted using a lumped parameter model of the cardiovascular system (CVS). The results demonstrated that the proposed control method efficiently drives an LVAD pump under accepted clinical conditions. In both scenarios, the left ventricle pressure recorded 112 mmHg for rest and 55 mmHg for exercise, and the systematic flow recorded 5.5 L/min for rest and 1.75 L/min for exercise.

In this study, we present a sensorless, robust, and physiological tracking control method to drive the operational speed of implantable rotary blood pumps (IRBPs) for patients with heart failure (HF). The method used sensorless measurements of the pump flow to track the desired reference flow ($Q_r$). A dynamical estimator model was used to estimate the average pump flow (${\hat{Q}}_{est}$) based on pulse-width modulation (PWM) signals. A proportional-integral (PI) controller integrated with a fuzzy logic control (FLC) system was developed to automatically adapt the pump flow. The $Q_r$ was modeled as a constant and trigonometric function using an elastance function ($E(t$)) to achieve a variation in the metabolic demand. The proposed method was evaluated in silico using a lumped parameter model of the cardiovascular system (CVS) under rest and exercise scenarios. The findings demonstrated that the proposed control system efficiently updated the pump speed of the IRBP to avoid suction or overperfusion. In all scenarios, the numerical results for the left atrium pressure ($P_{la}$), aortic pressure ($P_{ao}$), and left ventricle pressure ($P_{lv}$) were clinically accepted. The ${\hat{Q}}_{est}$ accurately tracked the $Q_r$ within an error of 0.25 L/min.