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.
Hand gesture recognition is a quickly developing field with many uses in human-computer interaction, sign language recognition, virtual reality, gaming, and robotics. This paper reviews different ways to model hands, such as vision-based, sensor-based, and data glove-based techniques. It emphasizes the importance of accurate hand modeling and feature extraction for capturing and analyzing gestures. Key features like motion, depth, color, shape, and pixel values and their relevance in gesture recognition are discussed. Challenges faced in hand gesture recognition include lighting variations, complex backgrounds, noise, and real-time performance. Machine learning algorithms are used to classify and recognize gestures based on extracted features. The paper emphasizes the need for further research and advancements to improve hand gesture recognition systems’ robustness, accuracy, and usability. This review offers valuable insights into the current state of hand gesture recognition, its applications, and its potential to revolutionize human-computer interaction and enable natural and intuitive interactions between humans and machines. In simpler terms, hand gesture recognition is a way for computers to understand what people are saying with their hands. It has many potential applications, such as allowing people to control computers without touching them or helping people with disabilities communicate. The paper reviews different ways to develop hand gesture recognition systems and discusses the challenges and opportunities in this area.