glovebased

Glove-based devices are wearable sensing systems designed to capture hand movements and interactions through a glove. They typically measure finger bending, hand pose, and sometimes contact forces, enabling natural input for virtual environments, teleoperation, robotics, and rehabilitation.

Most glove-based systems use a combination of sensors: flex or bend sensors along the fingers to gauge finger angles; magnetic, optical, or fiber-optic tracking to determine hand position and orientation; and sometimes inertial measurement units for additional motion data. Some designs include back-of-hand actuators or haptic elements to provide tactile feedback. Data collected by these sensors is transmitted to a computer or robot controller, where software interprets it via pose estimation or gesture recognition. Calibration is often required to map raw sensor readings to precise joint angles and movements.

History and applications: Data gloves emerged in the late 20th century as a means to capture natural hand motion for virtual reality and simulation. Notable examples include early data gloves used in research, such as the CyberGlove and the 5DT Data Glove. Glove-based interfaces are used in virtual reality and gaming, robotic teleoperation, surgical simulation, sign-language research and recognition, and rehabilitation therapy.

Challenges and future directions: Key issues include comfort and wearability, cost, weight, accuracy, and latency, with calibration remaining a practical hurdle. Trends point toward textile-based and soft-sensor gloves, flexible and stretchable electronics, and improved machine learning for reliable gesture recognition and hand pose estimation, along with enhanced haptic feedback to close the loop in human–glove interactions.