Rehabilitation robots have become one of the main technical instruments that Treat disorder patients in the biomedical engineering field. The robotic glove for the rehabilitation is basically made of specialized materials which can be designed to help the post-stroke patients. In this paper, a review of the different types of robotic glove for Rehabilitation have been discussed and summarized. This study reviews a different mechanical system of robotic gloves in previous years. The selected studies have been classified into four types according to the Mechanical Design: The first type is a tendon-driven robotic glove. The second type of robotic glove works with a soft actuator as a pneumatic which is operated by air pressure that passes through a plastic pipe, pressure valves, and air compressor. The third type is the exoskeleton robotic gloves this type consists of a wearable mechanical design that can used a finger-based sensor to measure grip strength or is used in interactive video applications. And the fourth type is the robotic glove with a liner actuator this type consists of a tape placed on the fingers and connected to linear actuators to open and close the fingers during the rehabilitation process.
Using a lower limb exoskeleton for rehabilitation (LLE) Lower limb exoskeleton rehabilitation robots (LER) are designed to assist patients with daily duties and help them regain their ability to walk. Even though a substantial portion of them is capable of doing both, they have not yet succeeded in conducting agile and intelligent joint movement between humans and machines, which is their ultimate goal. The typical LLE products, rapid prototyping, and cutting-edge techniques are covered in this review. Restoring a patient’s athletic prowess to its pr-accident level is the aim of rehabilitation treatment. The core of research on lower limb exoskeleton rehabilitation robots is the understanding of human gait. The performance of common prototypes might be used to match wearable robot shapes to human limbs. To imitate a normal stride, robot-assisted treatment needs to be able to control the movement of the robot at each joint and move the patient’s limb.
Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication; the BMI uses the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental tasks from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Two feature extraction algorithms using overlapped and non overlapped signal segments are analyzed. Principal component analysis is used for extracting features from the EEG signal segments. Classification performance of overlapping EEG signal segments is observed to be better in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications.
Soft robots, which are often considered safer than rigid robots when interacting with humans due to the reduced risk of injury, have found utility in various medical and industrial fields. Pneumatic artificial muscles (PAMs), one of the most widely used soft actuators, have proven their efficiency in numerous applications, including prosthetic and rehabilitation robots. PAMs are lightweight, responsive, precise, and capable of delivering a high force-to-weight ratio. Their structure comprises a flexible, inflatable membrane reinforced with fibrous twine and fitted with gas-sealing fittings. For the optimal design and integration of these into control systems, it is crucial to develop mathematical models that accurately represent their functioning mechanisms. This paper introduces a general concept of PAM’s construction, its various types, and operational mechanisms, along with its key benefits and drawbacks, and also reviews the most common modeling techniques for PAM representation. Most models are grounded in PAM architecture, aiming to calculate the actuator’s force across its full axis by correlating pressure, length, and other parameters that influence actuator strength.
The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.