Gesture recognition refers to the capacity of a device to recognize and process user input in the form of human gestures. These might be anything from a wave of the hand to a series of symbols drawn with the fingers. Cameras or other sensors record the user's movements, and then data is analyzed and interpreted by machine learning algorithms to enable gesture recognition. This article focuses on hand gesture recognition technologies and applications.

Hand gestures are a kind of nonverbal communication in which the location and orientation of the palm's central area and the fingers convey meaning. There are both stationary and moving characteristics of the hand gesture. Static hand gestures involve only the hand shape for transferring a certain message, whereas dynamic hand gestures are made up of a sequence of hand motions for conveying the message. Due to the cultural specificity and diversity of gestures, people have different ways of describing them. Real-time hand gesture detection refers to the capacity to recognize hand movements in the moment, without any lag time.

Types of hand gesture recognition technologies

Hand gestures allow technologies to be divided into two types: vision-based and sensor-based.

Vision-based

Vision-based hand gesture recognition technology enables computers or devices to interpret and understand human hand movements and gestures using visual input, typically from cameras or other imaging sensors. Such systems start by capturing visual data of the user's hand or hands. This data is usually obtained through cameras, depth sensors or other imaging devices. The quality of the input data is crucial for accurate recognition.

The gathered data undergoes initial processing aimed at refining its quality and mitigating noise. This phase encompasses operations like background subtraction, noise minimization and image enhancement, with the goal of isolating the hand or hands from the background. Following successful isolation, the system embarks on real-time detection and tracking of hand movements. This typically entails the identification and continuous monitoring of key hand features, such as the palm and fingers, as they evolve in position and orientation. Subsequently, the system transforms the monitored hand gestures into a format amenable to recognition and interpretation.

The core of the system is the gesture recognition algorithm, which matches the extracted hand gesture features against a predefined set of gestures or a trained machine learning model. This model can be based on various techniques, including neural networks, decision trees, or support vector machines. It determines which gesture or action the user is performing. Once a gesture is recognized, the system can trigger corresponding actions or interactions.

Sensor-based

Sensor-based hand gesture recognition refers to a technology that allows computers or devices to interpret and understand human hand movements and gestures using various sensors other than cameras. Unlike vision-based hand gesture recognition, which relies on visual data, these systems use different types of sensors to capture and analyze hand movements and gestures. For example, accelerometers and gyroscopes that measure changes in acceleration and rotation can be attached to the hand or a wearable device to track the orientation, movement and gestures of the hand.

Electromagnetic sensors pick up electrical signals originating from hand and forearm muscles. Through signal analysis, the system can deduce hand motions and gestures. Ultrasonic sensors emit high-frequency sound waves, gauging their return time after hitting objects. When placed around the hand, these sensors pinpoint the hand's position and nearby object movement. Magnetic sensors identify shifts in Earth's magnetic field from hand actions or magnetic object orientation. These sensors occasionally serve in activities such as 3D modeling and orientation tracking.

Applications of hand gesture recognition technologies

Hand gesture recognition technologies find diverse uses in healthcare and security realms, promising heightened efficiency, safety and user interaction. They empower individuals with disabilities, allowing those with motor impairments to navigate electric wheelchairs through intuitive hand gestures and facilitating effective communication for speech-impaired individuals. Gesture-based rehabilitation exercises receive precise monitoring and feedback through sensors, ensuring correct and safe execution. In healthcare, these technologies introduce touchless control for equipment, reducing infection risks and enabling professionals to interact with records and systems without direct contact. Surgeons employ hand gestures to manipulate surgical images, upholding sterility standards.

In security, gesture recognition augments access control, offering biometric authentication for secure premises. Security cameras with gesture analysis capabilities can identify suspicious actions and alert personnel. Additionally, behavior analysis in public spaces becomes more robust, detecting potential threats. At transportation hubs such as airports and border checkpoints, gesture recognition simplifies passenger processing, streamlining identity verification and gate control. The fusion of facial recognition and gestures enhances security systems. Finally, smart cities benefit from gesture-integrated surveillance, improving safety and public services by responding to gestures and actions in public areas.

Conclusion

Human gesture recognition technologies have the potential to enhance the naturalness and intuitiveness of human-computer interactions. Vision-based hand gesture recognition has seen significant advancements in recent years, thanks to improvements in camera technology, computer vision algorithms and machine learning techniques. Sensor-based hand gesture recognition has its advantages in situations where vision-based recognition may be challenging, such as low-light environments, occlusions or the need for precise control. Hand gesture recognition technologies offer valuable applications in healthcare and security. They aid individuals with disabilities, facilitate remote monitoring and promote infection control by reducing touch interactions. These technologies enhance access control through biometric authentication and improve surveillance by detecting suspicious gestures and behaviors.