Rehabilitation is the process of treating post-stroke consequences. Impaired limbs are considered the common outcomes of stroke, which require a professional therapist to rehabilitate the impaired limbs and restore fully or partially its function. Due to the shortage in the number of therapists and other considerations, researchers have been working on developing robots that have the ability to perform the rehabilitation process. During the last two decades, different robots were invented to help in rehabilitation procedures. This paper explains the types of rehabilitation treatments and robot classifications. In addition, a few examples of well-known rehabilitation robots will be explained in terms of their efficiency and controlling mechanisms.

The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves calculating the value of the integrated EMG (IEMG) to determine the continuous contraction of the muscle being investigated. It was found that the IEMG value continued to increase with prolonged muscle contraction and progressive fatigue. The second step involves differentiating between the high-frequency components (HFC) and low-frequency components (LFC) of the EMG, and calculating the fatigue index. Basically, the segmented EMG signal was filtered by two band-pass filters separately to produce two sub-signals, namely, a high-frequency sub-signal (HFSS) and a low-frequency sub-signal (LFSS). Then, the instantaneous mean amplitude (IMA) was calculated for the two sub-signals. The proposed algorithm indicates that the IMA of the HFSS tends to decrease during muscle fatigue, while the IMA of the LFSS tends to increase. The fatigue index represents the difference between the IMA values of the LFSS and HFSS, respectively. Muscle fatigue was found to be present and was objectively detected when the value of the proposed fatigue index was equal to or greater than zero. The proposed algorithm was tested on 75 EMG signals that were extracted from 75 middle deltoid muscles. The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue.

Classifying medical datasets using machine learning algorithms could help physicians to provide accurate diagnosing and suitable treatment. For instance, stroke is one of the serious diseases that attacks many patients annually, and analyzing it is symptoms in advance could save patients’ lives. The warning signs of the stroke can be investigated to be used as attributes or predictors for machine learning models. This study evaluates the performance of four machine learning models to classify stroke datasets. Specifically, Decision Tree, Naïve Bayes, K- Nearest Neighbor (KNN) and Linear discriminant Analyses (LDA) models were trained on 11 attributes collected from 5110 patients to predict stroke risk. The findings showed that KNN outperformed the three other models with an achieved accuracy of 90%. The study also considered balancing the employed data prior validating the models to provide accurate classification. Cross-validation technique was used to avoid over-fitting and under-fitting during training phases.

Introduction: Chronic obstructive pulmonary diseases are the most common disease worldwide. Asthma and sleep apnea are the most prevalent of pulmonary diseases. Patients with such chronic diseases require special care and continuous monitoring to avoid any respiratory deterioration. Therefore, the development of a dedicated and reliable sensor with the aid of modern technologies for measuring and monitoring respiratory parameters is very necessary nowadays. Objective: This study aims to develop a small and cost-effective respiratory rate sensor. Methods: A microcontroller and communication technology (NodeMCU) with the ThingSpeak platform is used in the proposed system to view and process the respiratory rate data every 60 s. The total current consumption of the proposed sensor is about 120 mA. Four able-bodied participants were recruited to test and validate the developed system. Results: The results show that the developed sensor and the proposed system can be used to measure and monitor the respiratory rate. Conclusions: The demonstrated system showed applicable, repeatable, and acceptable results.

Disability, specifically impaired upper and/or lower limbs, has a direct impact on the patients’ quality of life. Nowadays, motorized wheelchairs supported by a mobility-aided technique have been devised to improve the quality of life of these patients by increasing their independence. This study aims to present a platform to control a motorized wheelchair based on face tilting. A real-time tracking system of face tilting using a webcam and a microcontroller circuit has been designed and implemented. The designed system is dedicated to control the movement directions of the motorized wheelchair. Four commands were adequate to perform the required movements for the motorized wheelchair (forward, right, and left, as well as stopping status). The platform showed an excellent performance regarding controlling the motorized wheelchair using face tilting, and the position of the eyes was shown as the most useful face feature to track face tilting.